survey entry 0
type: SurveyResponse
date created: 2025-12-02T10:10:58.471
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: < 50 employees
teamfocus: Data quality / reliability, Analytics / BI
storageenvironment: Cloud PostgreSQL/MySQL
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: None / modeling is going well
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: United States / Canada

survey entry 1
type: SurveyResponse
date created: 2025-12-02T10:14:58.545
industry: Tech
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Architect
orgsize: < 50 employees
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Mixed/hybrid
orchestration: As a consultant - entirely depends on the client
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: None? Depends on the client
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: The fundamentals.
region: United States / Canada

survey entry 2
type: SurveyResponse
date created: 2025-12-02T10:27:56.242
industry: Public sector / Education
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 200–999
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Weekly
aihelpswith: Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Architecture patterns
industrywish: Data Ownership is the foundation of everything
region: Europe (EU / UK)

survey entry 3
type: SurveyResponse
date created: 2025-12-02T10:28:09.813
industry: Manufacturing / Industrial
job title: Platform Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Platform Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Dagster
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Not sure
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 4
type: SurveyResponse
date created: 2025-12-02T10:28:11.896
industry: Public sector / Education
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Architect
orgsize: 10,000+
teamfocus: Data modeling / transformation, Data quality / reliability, Infrastructure / platform work, ML / AI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 5
type: SurveyResponse
date created: 2025-12-02T10:28:57.269
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: 200–999
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: Canonical/semantic models
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Data modeling
region: United States / Canada

survey entry 6
type: SurveyResponse
date created: 2025-12-02T10:28:59.203
industry: Finance
job title: BI Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: BI Engineer
orgsize: 10,000+
teamfocus: Data modeling / transformation, Data quality / reliability, Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Governance / quality checks
aiadoption: AI embedded in most workflows
modelingapproach: Canonical/semantic models
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Data quality
teamgrowth2026: Grow
educationtopic: Data modeling
industrywish: It is very diverse and not generic at all
region: Asia–Pacific

survey entry 7
type: SurveyResponse
date created: 2025-12-02T10:29:09.271
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: Analytics Engineer
orgsize: 200–999
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Data modeling
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Not sure
educationtopic: Data modeling
region: Latin America

survey entry 8
type: SurveyResponse
date created: 2025-12-02T10:30:02.255
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: < 50 employees
teamfocus: Infrastructure / platform work, ML / AI
storageenvironment: On-prem data lake / lakehouse
orchestration: Kubeflow (open for alternatives)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: It shouldn't be an afterthought
region: Europe (EU / UK)

survey entry 9
type: SurveyResponse
date created: 2025-12-02T10:31:48.878
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 50–199
teamfocus: Data modeling / transformation, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Dagster
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery, Architecture design
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”, AI tools produce inconsistent schemas
architecturetrend: Event-driven architecture feeding into a centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: Data modeling is not the same thing as a pipeline
region: United States / Canada

survey entry 10
type: SurveyResponse
date created: 2025-12-02T10:33:37.260
industry: Finance
job title: Team of 1
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Team of 1
orgsize: < 50 employees
teamfocus: Data quality / reliability, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Git Hub Actions
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: None / modeling is going well
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 11
type: SurveyResponse
date created: 2025-12-02T10:34:04.857
industry: Tech
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery, Data modeling
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Shrink
educationtopic: Semantics / ontologies / knowledge graphs
region: Europe (EU / UK)

survey entry 12
type: SurveyResponse
date created: 2025-12-02T10:36:22.627
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 1,000–10,000
teamfocus: Analytics / BI, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Building internal AI platforms
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: Europe (EU / UK)

survey entry 13
type: SurveyResponse
date created: 2025-12-02T10:37:11.828
industry: Finance
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 200–999
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Cost cutting, not enough hands
teamgrowth2026: Not sure
educationtopic: AI/LLM integration
industrywish: Data is like water, it flows. But if not controlled it will pop up/show unexpectedly (because business is also like water, it always finds its way) multiplying operational costs and risks.
region: Europe (EU / UK)

survey entry 14
type: SurveyResponse
date created: 2025-12-02T10:38:27.592
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 10,000+
teamfocus: Data quality / reliability, ML / AI, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”, AI tools produce inconsistent schemas
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Shrink
educationtopic: Architecture patterns
industrywish: 1. DE for AI requires a different way of thinking (and then building). 2. DEs can no longer work in isolation. They need to work with their upstream and downstream partners. Great DEs always knew this, but many still operate as if they are some engineer who doesn’t need to care about what happens either side of their work.
region: United States / Canada

survey entry 15
type: SurveyResponse
date created: 2025-12-02T10:38:39.067
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: Data Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud PostgreSQL/MySQL
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Shrink
educationtopic: Career growth / leadership
industrywish: that thing you said about data modeling as a program, not a project
region: Latin America

survey entry 16
type: SurveyResponse
date created: 2025-12-02T10:38:55.540
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: 50–199
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Pressure to “move fast”, AI tools produce inconsistent schemas
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Career growth / leadership
region: United States / Canada

survey entry 17
type: SurveyResponse
date created: 2025-12-02T10:39:01.033
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 50–199
teamfocus: Data modeling / transformation, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Rarely
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Event-driven architecture
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Not sure
educationtopic: Data modeling
region: United States / Canada

survey entry 18
type: SurveyResponse
date created: 2025-12-02T10:39:22.964
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 200–999
teamfocus: Data quality / reliability, Analytics / BI
storageenvironment: On-prem data warehouse
orchestration: Control m
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: No meaningful adoption yet
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: Shitty processes shitty data
region: Europe (EU / UK)

survey entry 19
type: SurveyResponse
date created: 2025-12-02T10:39:38.613
industry: Retail/e-commerce
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Rarely
aihelpswith: I don't find AI helpful
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 20
type: SurveyResponse
date created: 2025-12-02T10:39:47.590
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Data modeling
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: Data engineering will likely be the mechanism to ensure organizations can produce useful results with AI. AI will change data engineering but won't eliminate it.
region: United States / Canada

survey entry 21
type: SurveyResponse
date created: 2025-12-02T10:40:23.706
industry: Finance
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Architect
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: United States / Canada

survey entry 22
type: SurveyResponse
date created: 2025-12-02T10:40:32.673
industry: Manufacturing / Industrial
job title: Analyst
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analyst
orgsize: 1,000–10,000
teamfocus: Analytics / BI, Fighting fires
storageenvironment: Mixed/hybrid
orchestration: No orchestration / ad-hoc
aiusagefrequency: Rarely
aihelpswith: Documentation / data discovery, Architecture design
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Architecture patterns
region: United States / Canada

survey entry 23
type: SurveyResponse
date created: 2025-12-02T10:40:59.011
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: < 50 employees
teamfocus: Infrastructure / platform work, ML / AI
storageenvironment: Warehouse is overselling S3 and EFS
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: None / modeling is going well
architecturetrend: Main org is small and research-heavy. We use simple object storage for public datasets.
biggestbottleneck: Compute costs
teamgrowth2026: Grow
educationtopic: Architecture patterns
industrywish: It's a much broader discipline than most people think.
region: United States / Canada

survey entry 24
type: SurveyResponse
date created: 2025-12-02T10:41:07.325
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 25
type: SurveyResponse
date created: 2025-12-02T10:41:23.150
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 10,000+
teamfocus: Data quality / reliability, Analytics / BI, Fighting fires
storageenvironment: On-prem data warehouse
orchestration: No orchestration / ad-hoc
aiusagefrequency: Rarely
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Building internal AI platforms
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Architecture patterns
industrywish: I don't know if I can speak to the wider industry, but, within my organization, they don't understand that it is a separate important part of an overarching architecture.
region: Europe (EU / UK)

survey entry 26
type: SurveyResponse
date created: 2025-12-02T10:42:02.006
industry: Finance
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Middle East Africa
role: Data Architect
orgsize: 200–999
teamfocus: Ingestion / pipelines, Analytics / BI
storageenvironment: Mixed/hybrid
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Data modeling
aiadoption: Using AI for tactical tasks
modelingapproach: Canonical/semantic models
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: Middle East / Africa

survey entry 27
type: SurveyResponse
date created: 2025-12-02T10:42:09.965
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), I don't find AI helpful
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: how much work a small change can be
region: Europe (EU / UK)

survey entry 28
type: SurveyResponse
date created: 2025-12-02T10:46:53.088
industry: Retail/e-commerce
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Analytics / BI, ML / AI, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery
aiadoption: AI embedded in most workflows
modelingapproach: One Big Table
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: More about supporting decisions and making direct impact, less discussion about tools
region: Europe (EU / UK)

survey entry 29
type: SurveyResponse
date created: 2025-12-02T10:47:14.100
industry: Public sector / Education
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, ML / AI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery, Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Canonical/semantic models
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: Europe (EU / UK)

survey entry 30
type: SurveyResponse
date created: 2025-12-02T10:47:57.934
industry: Retail/e-commerce
job title: Data Analyst / BI Developer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Analyst / BI Developer
orgsize: 10,000+
teamfocus: Data modeling / transformation, Data quality / reliability, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Not sure
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
industrywish: That good engineers need to understand the business in addition to the tech they use.
region: Europe (EU / UK)

survey entry 31
type: SurveyResponse
date created: 2025-12-02T10:50:13.091
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: < 50 employees
teamfocus: Data quality / reliability, Infrastructure / platform work, Fighting fires
storageenvironment: Data lake on S3 w/ athena
orchestration: Custom AWS state machine workflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Reliability engineering
industrywish: How to define with leadership and compile a centralized source of truth for data (system of record)
region: United States / Canada

survey entry 32
type: SurveyResponse
date created: 2025-12-02T10:52:37.058
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: Data Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: Latin America

survey entry 33
type: SurveyResponse
date created: 2025-12-02T10:56:09.347
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation, ML / AI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Temporal
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Data modeling, Architecture design
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Career growth / leadership
industrywish: We are not data science
region: Europe (EU / UK)

survey entry 34
type: SurveyResponse
date created: 2025-12-02T10:56:54.092
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data quality / reliability
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Databricks
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Data modeling
industrywish: We are the backbone for good data used by ML and AI.
region: United States / Canada

survey entry 35
type: SurveyResponse
date created: 2025-12-02T10:57:06.465
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, ML / AI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: AI embedded in most workflows
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Shrink
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 36
type: SurveyResponse
date created: 2025-12-02T11:00:07.318
industry: Healthcare
job title: early startup founder
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: early startup founder
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, ML / AI
storageenvironment: Mixed/hybrid
orchestration: Dagster
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Building internal AI platforms
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling, AI tools produce inconsistent schemas
architecturetrend: Event-driven architecture
biggestbottleneck: Tool complexity
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: Seems like every tool in LLM/AI space requires a K8s cluster with half the Apache foundation to do something a basic CMS could do. Most of the problems are not big enough for that, most companies are not going to drop > $300/m on infra just to manage their prompts.
region: United States / Canada

survey entry 37
type: SurveyResponse
date created: 2025-12-02T11:01:22.863
industry: Manufacturing / Industrial
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Data quality / reliability, ML / AI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Architecture design
aiadoption: No meaningful adoption yet
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Data modeling
region: United States / Canada

survey entry 38
type: SurveyResponse
date created: 2025-12-02T11:02:47.681
industry: Manufacturing / Industrial
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 200–999
teamfocus: Data quality / reliability, Fighting fires
storageenvironment: On-prem data warehouse
orchestration: SSIS
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Architecture patterns
industrywish: You cannot build fast, good and cheap.
region: Europe (EU / UK)

survey entry 39
type: SurveyResponse
date created: 2025-12-02T11:05:18.341
industry: Healthcare
job title: Software Engineer working on data
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Software Engineer working on data
orgsize: 200–999
teamfocus: Ingestion / pipelines, Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Data modeling, Governance / quality checks
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Pressure to “move fast”
architecturetrend: Event-driven architecture
biggestbottleneck: Compute costs
teamgrowth2026: Not sure
educationtopic: Streaming / event-driven systems
region: United States / Canada

survey entry 40
type: SurveyResponse
date created: 2025-12-02T11:05:24.289
industry: Finance
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Never
aihelpswith: I don't find AI helpful
aiadoption: No meaningful adoption yet
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Tools don’t support good modeling
architecturetrend: a message table per report
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Data modeling
industrywish: The many d different roles engineers play
region: United States / Canada

survey entry 41
type: SurveyResponse
date created: 2025-12-02T11:08:59.000
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: Event-driven modeling
modelingpainpoints: None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: Compute costs
teamgrowth2026: Stay the same
educationtopic: Streaming / event-driven systems
region: Latin America

survey entry 42
type: SurveyResponse
date created: 2025-12-02T11:10:13.167
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: AI embedded in most workflows
modelingapproach: Event-driven modeling
modelingpainpoints: Lack of clear ownership
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Streaming / event-driven systems
region: Europe (EU / UK)

survey entry 43
type: SurveyResponse
date created: 2025-12-02T11:11:10.864
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Data modeling
aiadoption: Using AI for tactical tasks
modelingapproach: One Big Table
modelingpainpoints: Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Data quality
teamgrowth2026: Grow
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 44
type: SurveyResponse
date created: 2025-12-02T11:11:42.290
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 200–999
teamfocus: Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Tools don’t support good modeling
architecturetrend: Centralized warehouse
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
industrywish: Successful teams vs unsuccessful teams are often separated by operational discipline.
region: United States / Canada

survey entry 45
type: SurveyResponse
date created: 2025-12-02T11:12:01.123
industry: Healthcare
job title: Platform Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Platform Engineer
orgsize: 10,000+
teamfocus: Data modeling / transformation, Data quality / reliability, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 46
type: SurveyResponse
date created: 2025-12-02T11:13:21.781
industry: Healthcare
job title: Platform Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Platform Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Dagster
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Not sure
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 47
type: SurveyResponse
date created: 2025-12-02T11:13:53.945
industry: Public sector / Education
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Governance / quality checks
aiadoption: Experimenting
modelingapproach: One Big Table
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Event-driven architecture
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: Asia–Pacific

survey entry 48
type: SurveyResponse
date created: 2025-12-02T11:14:14.671
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 200–999
teamfocus: Data modeling / transformation, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Not enough people
teamgrowth2026: Stay the same
educationtopic: Streaming / event-driven systems
region: United States / Canada

survey entry 49
type: SurveyResponse
date created: 2025-12-02T11:14:41.146
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, ML / AI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Matillion
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 50
type: SurveyResponse
date created: 2025-12-02T11:15:36.082
industry: Public sector / Education
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Tool complexity
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: United States / Canada

survey entry 51
type: SurveyResponse
date created: 2025-12-02T11:16:57.586
industry: Healthcare
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data quality / reliability, Analytics / BI, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
region: Europe (EU / UK)

survey entry 52
type: SurveyResponse
date created: 2025-12-02T11:19:02.632
industry: Retail/e-commerce
job title: ML Engineer / MLOps
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: ML Engineer / MLOps
orgsize: 10,000+
teamfocus: Data modeling / transformation, ML / AI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Architecture patterns
region: United States / Canada

survey entry 53
type: SurveyResponse
date created: 2025-12-02T11:19:04.032
industry: Tech
job title: Platform Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Platform Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Infrastructure / platform work, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Architecture design
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Career growth / leadership
industrywish: SaaS vendors won't solve your problems - it matters very little whether you use Snowflake, DataBricks, AWS or GCP.
region: Europe (EU / UK)

survey entry 54
type: SurveyResponse
date created: 2025-12-02T11:19:25.086
industry: Healthcare
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Infrastructure / platform work
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 55
type: SurveyResponse
date created: 2025-12-02T11:21:09.210
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: 50–199
teamfocus: Analytics / BI, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: dbt
aiusagefrequency: Multiple times per day
aihelpswith: Pipeline debugging, Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: United States / Canada

survey entry 56
type: SurveyResponse
date created: 2025-12-02T11:21:47.013
industry: Manufacturing / Industrial
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 200–999
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Fabric
orchestration: Fabric data pipelines
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Not sure
educationtopic: Data modeling
region: United States / Canada

survey entry 57
type: SurveyResponse
date created: 2025-12-02T11:23:39.928
industry: Tech
job title: Software Engineer working on data
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Software Engineer working on data
orgsize: 1,000–10,000
teamfocus: Infrastructure / platform work, Fighting fires
storageenvironment: On-prem data warehouse
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Reliability engineering
region: Europe (EU / UK)

survey entry 58
type: SurveyResponse
date created: 2025-12-02T11:23:46.873
industry: Tech
job title: Platform Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Platform Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines, Data modeling / transformation, Infrastructure / platform work, Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Prefect
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Data modeling
aiadoption: No meaningful adoption yet
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Streaming / event-driven systems
region: Europe (EU / UK)

survey entry 59
type: SurveyResponse
date created: 2025-12-02T11:23:51.163
industry: Tech
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Architect
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Reliability engineering
region: United States / Canada

survey entry 60
type: SurveyResponse
date created: 2025-12-02T11:24:12.259
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership
architecturetrend: Lakehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 61
type: SurveyResponse
date created: 2025-12-02T11:26:01.862
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Analytics / BI, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Dagster
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 62
type: SurveyResponse
date created: 2025-12-02T11:26:11.724
industry: Public sector / Education
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Infrastructure / platform work
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: Fixing problems upstream is expensive
region: Europe (EU / UK)

survey entry 63
type: SurveyResponse
date created: 2025-12-02T11:26:12.836
industry: Healthcare
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 50–199
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: GitHub Actions
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Not sure
educationtopic: Data modeling
region: United States / Canada

survey entry 64
type: SurveyResponse
date created: 2025-12-02T11:27:33.129
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: Data Engineer
orgsize: < 50 employees
teamfocus: Ingestion / pipelines
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Databricks Workflows
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: Latin America

survey entry 65
type: SurveyResponse
date created: 2025-12-02T11:28:38.203
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Streaming / event-driven systems
region: United States / Canada

survey entry 66
type: SurveyResponse
date created: 2025-12-02T11:28:53.437
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Streaming / event-driven systems
region: United States / Canada

survey entry 67
type: SurveyResponse
date created: 2025-12-02T11:29:22.750
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Fighting fires
storageenvironment: Mixed/hybrid
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery
aiadoption: No meaningful adoption yet
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Shrink
educationtopic: Architecture patterns
region: United States / Canada

survey entry 68
type: SurveyResponse
date created: 2025-12-02T11:29:45.143
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: < 50 employees
teamfocus: Data quality / reliability, Infrastructure / platform work
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Data modeling
region: United States / Canada

survey entry 69
type: SurveyResponse
date created: 2025-12-02T11:30:18.103
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: < 50 employees
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Prefect
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Event-driven architecture
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Not sure
educationtopic: Data modeling
industrywish: Data models, and engineering are the foundation of ML and AI. Recent RIFs have both heavily targeted DE teams, while asking for "better" ML and "more" AI models
region: United States / Canada

survey entry 70
type: SurveyResponse
date created: 2025-12-02T11:33:12.828
industry: Tech
job title: BI LEADER
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: BI LEADER
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Mixed/hybrid
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: Latin America

survey entry 71
type: SurveyResponse
date created: 2025-12-02T11:33:23.338
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: < 50 employees
teamfocus: Analytics / BI, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Governance / quality checks
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 72
type: SurveyResponse
date created: 2025-12-02T11:33:44.412
industry: Finance
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 50–199
teamfocus: Data quality / reliability, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Lambda w/ CRON
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: United States / Canada

survey entry 73
type: SurveyResponse
date created: 2025-12-02T11:33:59.539
industry: Tech
job title: All of above
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: All of above
orgsize: < 50 employees
teamfocus: Data modeling / transformation, ML / AI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Orchestra
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: AI embedded in most workflows
modelingapproach: Canonical/semantic models
modelingpainpoints: Tools don’t support good modeling
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: Probably just a better understanding of the basics to enable them to fathom "what is possible" and the requirements to achieving that.
region: United States / Canada

survey entry 74
type: SurveyResponse
date created: 2025-12-02T11:35:30.811
industry: Tech
job title: Software Engineer working on data
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Software Engineer working on data
orgsize: < 50 employees
teamfocus: Data modeling / transformation, ML / AI
storageenvironment: Mixed/hybrid
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Data modeling, Governance / quality checks
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, AI tools produce inconsistent schemas, None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
region: Europe (EU / UK)

survey entry 75
type: SurveyResponse
date created: 2025-12-02T11:36:03.358
industry: Manufacturing / Industrial
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 10,000+
teamfocus: Analytics / BI, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Databricks Jobs
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: No meaningful adoption yet
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: How multi-faceted and advanced it is. It is hard to do good engineering.
region: Europe (EU / UK)

survey entry 76
type: SurveyResponse
date created: 2025-12-02T11:38:46.200
industry: Public sector / Education
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Infrastructure / platform work, Analytics / BI, Fighting fires
storageenvironment: Mixed/hybrid
orchestration: No orchestration / ad-hoc
aiusagefrequency: Weekly
aihelpswith: Governance / quality checks
aiadoption: No meaningful adoption yet
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling, Pressure to “move fast”, AI tools produce inconsistent schemas
architecturetrend: Not invested in
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Not sure
educationtopic: AI/LLM integration
industrywish: How valuable it is to get real insights
region: Europe (EU / UK)

survey entry 77
type: SurveyResponse
date created: 2025-12-02T11:38:52.347
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: It’s more than just moving data from A to B in hopes it becomes useful as a result
region: Europe (EU / UK)

survey entry 78
type: SurveyResponse
date created: 2025-12-02T11:38:58.120
industry: Tech
job title: Agile Data Coach
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Agile Data Coach
orgsize: < 50 employees
teamfocus: Data modeling / transformation, Infrastructure / platform work, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: AgileData.cloud
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery, Data modeling, Architecture design
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Tools don’t support good modeling
architecturetrend: Centralized warehouse
biggestbottleneck: Tool complexity
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
region: Europe (EU / UK)

survey entry 79
type: SurveyResponse
date created: 2025-12-02T11:39:13.182
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data quality / reliability
storageenvironment: Mixed/hybrid
orchestration: Prefect
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Event-driven architecture
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Shrink
educationtopic: Architecture patterns
region: Europe (EU / UK)

survey entry 80
type: SurveyResponse
date created: 2025-12-02T11:39:30.005
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: < 50 employees
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: dbt cloud + AWS DMS
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Shrink
educationtopic: Career growth / leadership
region: Europe (EU / UK)

survey entry 81
type: SurveyResponse
date created: 2025-12-02T11:40:10.780
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: glue jobs scheduler
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Experimenting
modelingapproach: Event-driven modeling
modelingpainpoints: None / modeling is going well
architecturetrend: Event-driven architecture
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Not sure
educationtopic: AI/LLM integration
industrywish: what we are actually doing because it is not visible
region: Europe (EU / UK)

survey entry 82
type: SurveyResponse
date created: 2025-12-02T11:40:23.161
industry: Retail/e-commerce
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 83
type: SurveyResponse
date created: 2025-12-02T11:40:35.736
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines, ML / AI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Dagster
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Governance / quality checks
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Reliability engineering
region: United States / Canada

survey entry 84
type: SurveyResponse
date created: 2025-12-02T11:40:37.828
industry: Tech
job title: Software Engineer working on data
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Software Engineer working on data
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Homemade; custom solution to solve dbt's shortcomings
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
industrywish: it's all a people problem
region: United States / Canada

survey entry 85
type: SurveyResponse
date created: 2025-12-02T11:41:12.615
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Analytics / BI
storageenvironment: On-prem data warehouse
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling
architecturetrend: Lakehouse
biggestbottleneck: Data quality
teamgrowth2026: Not sure
educationtopic: Data modeling
region: Latin America

survey entry 86
type: SurveyResponse
date created: 2025-12-02T11:41:23.024
industry: Public sector / Education
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Analytics / BI
storageenvironment: Mixed/hybrid
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Tool complexity
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: Its depth
region: United States / Canada

survey entry 87
type: SurveyResponse
date created: 2025-12-02T11:43:52.094
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: Manager / Director / VP
orgsize: < 50 employees
teamfocus: Infrastructure / platform work, Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Event-driven architecture
biggestbottleneck: Compute costs
teamgrowth2026: Grow
educationtopic: Streaming / event-driven systems
region: Latin America

survey entry 88
type: SurveyResponse
date created: 2025-12-02T11:44:09.792
industry: Retail/e-commerce
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 1,000–10,000
teamfocus: Data quality / reliability, Analytics / BI, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”, AI tools produce inconsistent schemas
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Compute costs
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: Europe (EU / UK)

survey entry 89
type: SurveyResponse
date created: 2025-12-02T11:45:15.832
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Infrastructure / platform work, Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Data modeling, Architecture design, Governance / quality checks
aiadoption: No meaningful adoption yet
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: Latin America

survey entry 90
type: SurveyResponse
date created: 2025-12-02T11:48:18.409
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Event-driven architecture
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 91
type: SurveyResponse
date created: 2025-12-02T11:52:47.401
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 10,000+
teamfocus: Data quality / reliability, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), I don't find AI helpful
aiadoption: Building internal AI platforms
modelingapproach: Event-driven modeling
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”, None / modeling is going well
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Architecture patterns
industrywish: Semantics, Ontologies, and Knowledge Graphs, and the design exercises they require - "always be architecting"
region: United States / Canada

survey entry 92
type: SurveyResponse
date created: 2025-12-02T11:53:03.449
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 50–199
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 93
type: SurveyResponse
date created: 2025-12-02T11:53:34.240
industry: Finance
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: 50–199
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
industrywish: Better understanding of how DE/AE positively impact the business and revenue/operations
region: United States / Canada

survey entry 94
type: SurveyResponse
date created: 2025-12-02T11:54:34.741
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Infrastructure / platform work, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Controlm
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Bureaucracy
teamgrowth2026: Stay the same
educationtopic: Streaming / event-driven systems
industrywish: Good data engineering is a prerequisite for adopting AI/ML.
region: Europe (EU / UK)

survey entry 95
type: SurveyResponse
date created: 2025-12-02T11:56:18.500
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: < 50 employees
teamfocus: Data quality / reliability, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: hand rolled, moving to DataForm
aiusagefrequency: Weekly
aihelpswith: I don't find AI helpful
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Compute costs
teamgrowth2026: Grow
educationtopic: Data modeling
industrywish: How hard data cleaning is
region: United States / Canada

survey entry 96
type: SurveyResponse
date created: 2025-12-02T11:56:26.366
industry: Public sector / Education
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Manager / Director / VP
orgsize: 10,000+
teamfocus: Analytics / BI, Fighting fires
storageenvironment: On-prem data warehouse
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Architecture design
aiadoption: Experimenting
modelingapproach: One Big Table
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: Australia / New Zealand

survey entry 97
type: SurveyResponse
date created: 2025-12-02T11:56:52.367
industry: Finance
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Architect
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Analytics / BI, Fighting fires
storageenvironment: On-prem data warehouse
orchestration: WhereScape
aiusagefrequency: Rarely
aihelpswith: Documentation / data discovery, Architecture design
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: Asia–Pacific

survey entry 98
type: SurveyResponse
date created: 2025-12-02T11:56:56.588
industry: Healthcare
job title: CDO
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: CDO
orgsize: 200–999
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: On-prem data warehouse
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Documentation / data discovery, Data modeling
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Tools don’t support good modeling
architecturetrend: Centralized warehouse
biggestbottleneck: Compute costs
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: United States / Canada

survey entry 99
type: SurveyResponse
date created: 2025-12-02T11:58:31.016
industry: Tech
job title: Software Engineer working on data
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Software Engineer working on data
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Infrastructure / platform work
storageenvironment: Mixed/hybrid
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Architecture design, Governance / quality checks
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership
architecturetrend: Lakehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Career growth / leadership
industrywish: It is not an afterthought endeavour. It is the backbone of any AI initiative.
region: Europe (EU / UK)

survey entry 100
type: SurveyResponse
date created: 2025-12-02T11:59:26.474
industry: Retail/e-commerce
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 10,000+
teamfocus: Infrastructure / platform work, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Streaming / event-driven systems
industrywish: In data engineering, a good design is one that adapts well to change
region: Europe (EU / UK)

survey entry 101
type: SurveyResponse
date created: 2025-12-02T12:01:05.317
industry: Finance
job title: Associate
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Associate
orgsize: 200–999
teamfocus: Data quality / reliability, Analytics / BI
storageenvironment: Cloud PostgreSQL/MySQL
orchestration: No orchestration / ad-hoc
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: One Big Table
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: An executive said to me: It doesn't matter how good you become at the bitch work, you're still the bitch. Eye opening. (Was said with politeness and discussing managing different value systems)
region: United States / Canada

survey entry 102
type: SurveyResponse
date created: 2025-12-02T12:03:48.442
industry: Public sector / Education
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Fighting fires
storageenvironment: On-prem data warehouse
orchestration: WhereScape plus oen stuff
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 103
type: SurveyResponse
date created: 2025-12-02T12:06:43.624
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: < 50 employees
teamfocus: Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud functions, scheduling
aiusagefrequency: Multiple times per day
aihelpswith: Pipeline debugging, I don't find AI helpful
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, AI tools produce inconsistent schemas
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: data governance
region: Europe (EU / UK)

survey entry 104
type: SurveyResponse
date created: 2025-12-02T12:12:23.849
industry: Finance
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: Analytics Engineer
orgsize: 50–199
teamfocus: Analytics / BI, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: dbt Cloud
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Data quality
teamgrowth2026: Grow
educationtopic: Architecture patterns
region: Latin America

survey entry 105
type: SurveyResponse
date created: 2025-12-02T12:14:12.910
industry: Public sector / Education
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 50–199
teamfocus: Ingestion / pipelines, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Architecture design, Governance / quality checks
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling
architecturetrend: Lakehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 106
type: SurveyResponse
date created: 2025-12-02T12:14:50.769
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 107
type: SurveyResponse
date created: 2025-12-02T12:15:25.074
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: Data Engineer
orgsize: 50–199
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud PostgreSQL/MySQL
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Data modeling
industrywish: That demands a constant effort to keep the data flow working because data changes all the time.
region: Latin America

survey entry 108
type: SurveyResponse
date created: 2025-12-02T12:15:54.160
industry: Finance
job title: Data Analyst
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Analyst
orgsize: 50–199
teamfocus: Ingestion / pipelines, Analytics / BI
storageenvironment: On-prem data warehouse
orchestration: SSIS
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership
architecturetrend: Centralized warehouse
biggestbottleneck: Tool complexity
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: United States / Canada

survey entry 109
type: SurveyResponse
date created: 2025-12-02T12:16:50.588
industry: Tech
job title: Software Engineer working on data
sic: this
naics: this
occupational category: this
spatial coverage: Middle East Africa
role: Software Engineer working on data
orgsize: < 50 employees
teamfocus: Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Data modeling
aiadoption: No meaningful adoption yet
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: Middle East / Africa

survey entry 110
type: SurveyResponse
date created: 2025-12-02T12:17:11.682
industry: Tech
job title: Data & AI Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data & AI Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines, Data quality / reliability
storageenvironment: Cloud PostgreSQL/MySQL
orchestration: GitHub Workflows
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: No meaningful adoption yet
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: Most crucial, yet often overlooked
region: Europe (EU / UK)

survey entry 111
type: SurveyResponse
date created: 2025-12-02T12:22:34.774
industry: Tech
job title: Software Engineer working on data
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Software Engineer working on data
orgsize: 1,000–10,000
teamfocus: Infrastructure / platform work
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 112
type: SurveyResponse
date created: 2025-12-02T12:22:41.761
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Data Engineer
orgsize: 50–199
teamfocus: Data modeling / transformation, Infrastructure / platform work
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: Australia / New Zealand

survey entry 113
type: SurveyResponse
date created: 2025-12-02T12:25:11.079
industry: Finance
job title: Platform Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Platform Engineer
orgsize: 1,000–10,000
teamfocus: Infrastructure / platform work
storageenvironment: Mixed/hybrid
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time
architecturetrend: Centralized warehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Career growth / leadership
region: Europe (EU / UK)

survey entry 114
type: SurveyResponse
date created: 2025-12-02T12:26:57.118
industry: Tech
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Infrastructure / platform work
storageenvironment: Product database, analytics and integrations
orchestration: Python based pipelines
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: No meaningful adoption yet
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, None / modeling is going well
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: Growing convert into the power of data contracts and to some extent data products
region: Europe (EU / UK)

survey entry 115
type: SurveyResponse
date created: 2025-12-02T12:28:31.366
industry: Manufacturing / Industrial
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: 1,000–10,000
teamfocus: Analytics / BI
storageenvironment: Mixed/hybrid
orchestration: Microsoft SSIS
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Data modeling
aiadoption: No meaningful adoption yet
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Data modeling
industrywish: Need to work with Business folks in an integrated fashion and make them more curious about technical aspects that can lead to more Systemic thinking and not be reactive and merely demand reports on an adhoc basis(Ex. Slowly changing dimension; Data retention life cycle)
region: United States / Canada

survey entry 116
type: SurveyResponse
date created: 2025-12-02T12:28:41.186
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data quality / reliability
storageenvironment: On-prem data warehouse
orchestration: Ssis
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Reliability engineering
industrywish: Less pipeline, less problem
region: Europe (EU / UK)

survey entry 117
type: SurveyResponse
date created: 2025-12-02T12:29:42.031
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 200–999
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Data modeling, Architecture design, Governance / quality checks
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Career growth / leadership
region: Europe (EU / UK)

survey entry 118
type: SurveyResponse
date created: 2025-12-02T12:30:43.419
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 200–999
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Native tooling (i.e. dbt Cloud, Fivetran)
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Data modeling
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Architecture patterns
region: United States / Canada

survey entry 119
type: SurveyResponse
date created: 2025-12-02T12:34:58.005
industry: Retail/e-commerce
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation, Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery, Architecture design
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 120
type: SurveyResponse
date created: 2025-12-02T12:35:08.246
industry: Healthcare
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Architecture design, Governance / quality checks
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: Europe (EU / UK)

survey entry 121
type: SurveyResponse
date created: 2025-12-02T12:38:34.747
industry: Public sector / Education
job title: BI and Data engineering
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: BI and Data engineering
orgsize: 200–999
teamfocus: Data modeling / transformation
storageenvironment: On-prem data warehouse
orchestration: SQL Agent
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Not sure
educationtopic: AI/LLM integration
region: Australia / New Zealand

survey entry 122
type: SurveyResponse
date created: 2025-12-02T12:40:54.605
industry: Tech
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Data Architect
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Infrastructure / platform work, Analytics / BI, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Fabric
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Data modeling
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Reliability engineering
region: Australia / New Zealand

survey entry 123
type: SurveyResponse
date created: 2025-12-02T12:46:15.599
industry: Manufacturing / Industrial
job title: Product Analysts
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Product Analysts
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data quality / reliability, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Pressure to “move fast”
architecturetrend: Event-driven architecture
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Architecture patterns
region: Europe (EU / UK)

survey entry 124
type: SurveyResponse
date created: 2025-12-02T12:49:33.323
industry: Manufacturing / Industrial
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: No meaningful adoption yet
modelingapproach: One Big Table
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Data quality
teamgrowth2026: Grow
educationtopic: Data modeling
industrywish: Data modeling and how create a meaningful depiction of business processes in a data model
region: Europe (EU / UK)

survey entry 125
type: SurveyResponse
date created: 2025-12-02T12:51:48.142
industry: Healthcare
job title: AI Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: AI Engineer
orgsize: 10,000+
teamfocus: ML / AI
storageenvironment: Cloud PostgreSQL/MySQL
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time
architecturetrend: Event-driven architecture
biggestbottleneck: Data Access approvals
teamgrowth2026: Shrink
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: Data Modeling is everything
region: United States / Canada

survey entry 126
type: SurveyResponse
date created: 2025-12-02T12:53:26.298
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines, Data modeling / transformation, Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: None / modeling is going well
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 127
type: SurveyResponse
date created: 2025-12-02T12:55:59.711
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Data modeling, Architecture design, Governance / quality checks
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: United States / Canada

survey entry 128
type: SurveyResponse
date created: 2025-12-02T12:56:58.227
industry: Healthcare
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: ML / AI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Dagster
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Building internal AI platforms
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Career growth / leadership
industrywish: How versatile the role is. That it's really a specialized Software Engineering role, more than a Data Analyst role.
region: Europe (EU / UK)

survey entry 129
type: SurveyResponse
date created: 2025-12-02T12:57:00.543
industry: Public sector / Education
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Databricks pipelines
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling
architecturetrend: Centralized warehouse
biggestbottleneck: Tool complexity
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: Australia / New Zealand

survey entry 130
type: SurveyResponse
date created: 2025-12-02T12:58:15.653
industry: Public sector / Education
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Prefer not to say
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
industrywish: That tools aren't the problem; people and process are.
region: Prefer not to say

survey entry 131
type: SurveyResponse
date created: 2025-12-02T13:00:35.886
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Data modeling, Architecture design
aiadoption: No meaningful adoption yet
modelingapproach: Canonical/semantic models
modelingpainpoints: None / modeling is going well
architecturetrend: Lakehouse
biggestbottleneck: Compute costs
teamgrowth2026: Not sure
educationtopic: Architecture patterns
region: United States / Canada

survey entry 132
type: SurveyResponse
date created: 2025-12-02T13:02:20.117
industry: Manufacturing / Industrial
job title: Founder/CO’s LMW farm
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Founder/CO’s LMW farm
orgsize: < 50 employees
teamfocus: Data quality / reliability, Infrastructure / platform work, Analytics / BI, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design, Governance / quality checks
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Compute costs
teamgrowth2026: Stay the same
educationtopic: All the above
industrywish: We are your friends not your enemies
region: United States / Canada

survey entry 133
type: SurveyResponse
date created: 2025-12-02T13:05:15.671
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 10,000+
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Governance / quality checks
aiadoption: Building internal AI platforms
modelingapproach: One Big Table
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Shrink
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 134
type: SurveyResponse
date created: 2025-12-02T13:06:00.618
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Infrastructure / platform work, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Event bridge + sagemaker pipeline
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Data quality
teamgrowth2026: Grow
educationtopic: Data modeling
industrywish: Without data modeling we are ser to fail
region: Europe (EU / UK)

survey entry 135
type: SurveyResponse
date created: 2025-12-02T13:11:42.952
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: Data Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud PostgreSQL/MySQL
orchestration: No orchestration / ad-hoc
aiusagefrequency: Never
aihelpswith: I don't find AI helpful
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
industrywish: The difference between that and data science
region: Latin America

survey entry 136
type: SurveyResponse
date created: 2025-12-02T13:11:47.458
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Infrastructure / platform work, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Data modeling, Architecture design, Governance / quality checks
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Shrink
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 137
type: SurveyResponse
date created: 2025-12-02T13:12:07.483
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Event-driven architecture
biggestbottleneck: Data quality
teamgrowth2026: Grow
educationtopic: Streaming / event-driven systems
region: United States / Canada

survey entry 138
type: SurveyResponse
date created: 2025-12-02T13:16:19.423
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Data quality
teamgrowth2026: Grow
educationtopic: Career growth / leadership
region: Europe (EU / UK)

survey entry 139
type: SurveyResponse
date created: 2025-12-02T13:19:25.795
industry: Finance
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Analytics Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Dagster
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Building internal AI platforms
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Career growth / leadership
industrywish: It takes time to get right and doesn’t happen overnight. And if stakeholders pressure for immediate results, it’s Garbage In Garbage Out
region: Australia / New Zealand

survey entry 140
type: SurveyResponse
date created: 2025-12-02T13:20:54.440
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: < 50 employees
teamfocus: Data quality / reliability, Infrastructure / platform work, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Governance / quality checks
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Pressure to “move fast”
architecturetrend: Event-driven architecture
biggestbottleneck: Lack of time
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 141
type: SurveyResponse
date created: 2025-12-02T13:25:37.410
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: < 50 employees
teamfocus: Data modeling / transformation, ML / AI
storageenvironment: Cloud PostgreSQL/MySQL
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: AI embedded in most workflows
modelingapproach: One Big Table
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: Europe (EU / UK)

survey entry 142
type: SurveyResponse
date created: 2025-12-02T13:33:55.608
industry: Tech
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Building internal AI platforms
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Streaming / event-driven systems
region: Europe (EU / UK)

survey entry 143
type: SurveyResponse
date created: 2025-12-02T13:38:24.120
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation, Infrastructure / platform work
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Data modeling, Architecture design, Governance / quality checks
aiadoption: AI embedded in most workflows
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: Latin America

survey entry 144
type: SurveyResponse
date created: 2025-12-02T13:41:20.397
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 50–199
teamfocus: Data modeling / transformation, Data quality / reliability, Infrastructure / platform work, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Data modeling
aiadoption: Building internal AI platforms
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Streaming / event-driven systems
region: Europe (EU / UK)

survey entry 145
type: SurveyResponse
date created: 2025-12-02T13:41:56.902
industry: Retail/e-commerce
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Analytics / BI, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Canonical/semantic models
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 146
type: SurveyResponse
date created: 2025-12-02T13:42:17.046
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Shrink
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 147
type: SurveyResponse
date created: 2025-12-02T13:42:43.580
industry: Retail/e-commerce
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 200–999
teamfocus: Data modeling / transformation, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Native Snowflake
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Governance / quality checks
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: None / modeling is going well
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: Europe (EU / UK)

survey entry 148
type: SurveyResponse
date created: 2025-12-02T13:44:17.431
industry: Finance
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Canonical/semantic models
modelingpainpoints: Lack of clear ownership
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: Europe (EU / UK)

survey entry 149
type: SurveyResponse
date created: 2025-12-02T13:46:15.949
industry: Finance
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Governance / quality checks
aiadoption: Building internal AI platforms
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Architecture patterns
industrywish: Requirements/expectations must be clearly defined and backed-up with the appropriate resources.
region: Europe (EU / UK)

survey entry 150
type: SurveyResponse
date created: 2025-12-02T13:51:52.018
industry: Tech
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Architect
orgsize: 50–199
teamfocus: Ingestion / pipelines, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Databricks or dbtCloud
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery, Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 151
type: SurveyResponse
date created: 2025-12-02T13:58:11.674
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 50–199
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Dagster
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Streaming / event-driven systems
industrywish: Its never as "quick" as you think
region: United States / Canada

survey entry 152
type: SurveyResponse
date created: 2025-12-02T13:59:57.175
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Mixed/hybrid
orchestration: Dagster
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 153
type: SurveyResponse
date created: 2025-12-02T14:05:26.219
industry: Healthcare
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Architect
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Rarely
aihelpswith: Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: United States / Canada

survey entry 154
type: SurveyResponse
date created: 2025-12-02T14:08:39.286
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 200–999
teamfocus: Data quality / reliability, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Event-driven architecture
biggestbottleneck: Talent / hiring
teamgrowth2026: Not sure
educationtopic: Architecture patterns
industrywish: You can still do/be a data engineer without big tech tools and big tech buzzwords. If you can build a system/process that serves some end user a data product that is useful, insightful, or makes life easier for the customer or stakeholder then congrats! you are doing the thing.
region: United States / Canada

survey entry 155
type: SurveyResponse
date created: 2025-12-02T14:09:46.483
industry: Finance
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation, ML / AI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Data modeling, Governance / quality checks
aiadoption: Experimenting
modelingapproach: Canonical/semantic models
modelingpainpoints: Lack of clear ownership
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Compute costs
teamgrowth2026: Grow
educationtopic: Data modeling
region: United States / Canada

survey entry 156
type: SurveyResponse
date created: 2025-12-02T14:11:33.327
industry: Public sector / Education
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Analytics Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Analytics / BI
storageenvironment: On-prem data warehouse
orchestration: Alteryx
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
industrywish: That good quality data/analytics engineering takes time! Often I'm pressured to rush stuff and their is a consequent drop off in quality/maintainability.
region: Australia / New Zealand

survey entry 157
type: SurveyResponse
date created: 2025-12-02T14:18:35.266
industry: Tech
job title: Founder
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Founder
orgsize: < 50 employees
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Tools don’t support good modeling
architecturetrend: The thing that works for current situation
biggestbottleneck: Compute costs
teamgrowth2026: Grow
educationtopic: Data modeling
industrywish: Good data for testing will improve application quality
region: United States / Canada

survey entry 158
type: SurveyResponse
date created: 2025-12-02T14:22:13.242
industry: Tech
job title: Technical Account Manager
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Technical Account Manager
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, ML / AI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: No meaningful adoption yet
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 159
type: SurveyResponse
date created: 2025-12-02T14:31:18.068
industry: Healthcare
job title: Data Analyst
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Analyst
orgsize: 1,000–10,000
teamfocus: Analytics / BI, Fighting fires
storageenvironment: On-prem data warehouse
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Building internal AI platforms
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Tools don’t support good modeling
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Career growth / leadership
region: United States / Canada

survey entry 160
type: SurveyResponse
date created: 2025-12-02T14:31:53.533
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: DBT cloud
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 161
type: SurveyResponse
date created: 2025-12-02T14:34:22.942
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: < 50 employees
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Dagster
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Building internal AI platforms
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 162
type: SurveyResponse
date created: 2025-12-02T14:40:41.625
industry: Finance
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Matillion
aiusagefrequency: Rarely
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: One Big Table
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: Data Engineers are not just tech resources they need to be very tightly integrated to business processes
region: Europe (EU / UK)

survey entry 163
type: SurveyResponse
date created: 2025-12-02T14:50:19.708
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Infrastructure / platform work
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: databricks jobs
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Experimenting
modelingapproach: 3NF
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: data mesh failures
teamgrowth2026: Grow
educationtopic: Architecture patterns
region: Europe (EU / UK)

survey entry 164
type: SurveyResponse
date created: 2025-12-02T14:53:35.762
industry: Tech
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Architect
orgsize: 50–199
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Data modeling
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Event-driven architecture
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Data modeling
industrywish: Conceptual and logical data modeling are still exceptionally productive
region: United States / Canada

survey entry 165
type: SurveyResponse
date created: 2025-12-02T14:55:42.122
industry: Finance
job title: AI Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: AI Engineer
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Infrastructure / platform work, ML / AI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: None / modeling is going well
architecturetrend: Lakehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Reliability engineering
industrywish: AI readiness
region: Latin America

survey entry 166
type: SurveyResponse
date created: 2025-12-02T14:57:20.469
industry: Manufacturing / Industrial
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Architect
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: On-prem data warehouse
orchestration: Script based ETL
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: Data Vault
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
industrywish: First, there is no substitute for deep process knowledge. Second, not everyone has "Big Data" problems. In fact, the deeper your process knowledge becomes, the less data you need to make high impact business decisions.
region: United States / Canada

survey entry 167
type: SurveyResponse
date created: 2025-12-02T14:59:01.010
industry: Finance
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 10,000+
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: On-prem data warehouse
orchestration: DataStage
aiusagefrequency: Daily
aihelpswith: Documentation / data discovery, Governance / quality checks
aiadoption: Using AI for tactical tasks
modelingapproach: Canonical/semantic models
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Shrink
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 168
type: SurveyResponse
date created: 2025-12-02T15:02:14.157
industry: Finance
job title: AI Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: AI Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, ML / AI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Databricks Jobs
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Medallion
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Compute costs
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
industrywish: Stateful data pipelines
region: Latin America

survey entry 169
type: SurveyResponse
date created: 2025-12-02T15:02:56.538
industry: Public sector / Education
job title: Platform Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Platform Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: argo workflows.
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Data modeling, Architecture design, Governance / quality checks
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 170
type: SurveyResponse
date created: 2025-12-02T15:07:42.559
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 200–999
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Reliability engineering
industrywish: It’s as vast as the software engineering world except everything has a data twist, making it that much harder.
region: United States / Canada

survey entry 171
type: SurveyResponse
date created: 2025-12-02T15:16:45.832
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 200–999
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Mixed/hybrid
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Architecture patterns
industrywish: how difficult their requests can be. I know this is hopeless.
region: United States / Canada

survey entry 172
type: SurveyResponse
date created: 2025-12-02T15:17:29.200
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: 1,000–10,000
teamfocus: ML / AI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Governance / quality checks
aiadoption: AI embedded in most workflows
modelingapproach: Canonical/semantic models
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 173
type: SurveyResponse
date created: 2025-12-02T15:18:01.667
industry: Retail/e-commerce
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Architecture design, Governance / quality checks
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Event-driven architecture
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: Data Engineering is not Software Engineering
region: United States / Canada

survey entry 174
type: SurveyResponse
date created: 2025-12-02T15:23:07.618
industry: Tech
job title: Operational system designer, which doesnt seem to be a real job.
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Operational system designer, which doesnt seem to be a real job.
orgsize: < 50 employees
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: SQLServer
orchestration: The system itself
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Building internal AI platforms
modelingapproach: Bloody brilliant
modelingpainpoints: None / modeling is going well
architecturetrend: Ontology native convergence
biggestbottleneck: Data from other software tools.
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: That data engineering is a very young discipline and its time to throw away the 25 year old prototypes and to start thinking like an engineer. With data, the sequel.
region: Australia / New Zealand

survey entry 175
type: SurveyResponse
date created: 2025-12-02T15:25:30.509
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: < 50 employees
teamfocus: Data modeling / transformation, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Compute costs
teamgrowth2026: Grow
educationtopic: Streaming / event-driven systems
industrywish: That different use cases of data mean different models and tools, and that data can change over time over it's lifecycle, and that is fine. Not every data set will go through the same tools (ie a source of truth is not a system, it's a determined state in a lifecycle, which might be implemented in a different tool depending on the data) ie sometimes the correct truth source is Kafka, sometimes it's the warehouse.
region: Europe (EU / UK)

survey entry 176
type: SurveyResponse
date created: 2025-12-02T15:27:31.455
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 177
type: SurveyResponse
date created: 2025-12-02T15:51:50.383
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 10,000+
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: None / modeling is going well
architecturetrend: Event-driven architecture
biggestbottleneck: Talent / hiring
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
industrywish: It’s essential for data insights from AI to BI
region: United States / Canada

survey entry 178
type: SurveyResponse
date created: 2025-12-02T15:51:50.608
industry: Tech
job title: Data governance consultant (vendor)
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Data governance consultant (vendor)
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data quality / reliability
storageenvironment: Mixed/hybrid
orchestration: No orchestration / ad-hoc
aiusagefrequency: Weekly
aihelpswith: Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: Event-driven modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Not sure
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: You need an "any other comments" question: suggesting the "Which Industry" should be multi-choice?
region: Australia / New Zealand

survey entry 179
type: SurveyResponse
date created: 2025-12-02T15:53:19.357
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Engineer
orgsize: 10,000+
teamfocus: Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Data modeling
region: Asia–Pacific

survey entry 180
type: SurveyResponse
date created: 2025-12-02T16:16:54.789
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: 200–999
teamfocus: Data quality / reliability, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery, Governance / quality checks
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Data modeling
region: United States / Canada

survey entry 181
type: SurveyResponse
date created: 2025-12-02T16:44:00.664
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery, Architecture design
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 182
type: SurveyResponse
date created: 2025-12-02T16:48:42.012
industry: Tech
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Data Architect
orgsize: 50–199
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Snowflake Tasks
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: Tool complexity
teamgrowth2026: Grow
educationtopic: Reliability engineering
industrywish: Layered design rather than marketing driven design
region: Australia / New Zealand

survey entry 183
type: SurveyResponse
date created: 2025-12-02T16:57:30.429
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Career growth / leadership
industrywish: It’s an investment that will pay off if you do it well from inception and continue to do it well, without rushing it.
region: United States / Canada

survey entry 184
type: SurveyResponse
date created: 2025-12-02T17:00:14.319
industry: Healthcare
job title: BI Analyst
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: BI Analyst
orgsize: < 50 employees
teamfocus: Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: United States / Canada

survey entry 185
type: SurveyResponse
date created: 2025-12-02T17:21:58.921
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Prefer not to say
role: Data Engineer
orgsize: 10,000+
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: On-prem data warehouse
orchestration: Airflow
aiusagefrequency: Weekly
aihelpswith: Data modeling
aiadoption: Building internal AI platforms
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Streaming / event-driven systems
region: Prefer not to say

survey entry 186
type: SurveyResponse
date created: 2025-12-02T17:26:09.446
industry: Healthcare
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Combo of home-grown scheduler / Azure Data Factory
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: AI/LLM integration
industrywish: Data Engineering isn't about the tools you are using, but seems like most jobs require practice in tools.
region: United States / Canada

survey entry 187
type: SurveyResponse
date created: 2025-12-02T17:27:07.345
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: < 50 employees
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Mixed/hybrid
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Tools don’t support good modeling
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Not sure
educationtopic: AI/LLM integration
industrywish: Software engineers created all the problems data engineers have to deal with.
region: United States / Canada

survey entry 188
type: SurveyResponse
date created: 2025-12-02T17:30:49.843
industry: Retail/e-commerce
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Centralized warehouse
biggestbottleneck: Compute costs
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 189
type: SurveyResponse
date created: 2025-12-02T17:33:45.293
industry: Healthcare
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 190
type: SurveyResponse
date created: 2025-12-02T17:39:49.142
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 10,000+
teamfocus: Data modeling / transformation, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Data modeling, Architecture design
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: Needed and under-appreciated.
region: United States / Canada

survey entry 191
type: SurveyResponse
date created: 2025-12-02T17:41:10.271
industry: Public sector / Education
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 50–199
teamfocus: Ingestion / pipelines, Fighting fires
storageenvironment: Cloud PostgreSQL/MySQL
orchestration: No orchestration / ad-hoc
aiusagefrequency: Rarely
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Centralized warehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 192
type: SurveyResponse
date created: 2025-12-02T17:42:45.546
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation, Infrastructure / platform work, Analytics / BI, Fighting fires
storageenvironment: Azure SQL
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Data modeling
industrywish: Orgs suck at data in general and people dont seem to know xxxx about data in general (exceptions do exist).
region: Europe (EU / UK)

survey entry 193
type: SurveyResponse
date created: 2025-12-02T17:46:45.366
industry: Manufacturing / Industrial
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Dagster
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership
architecturetrend: Centralized warehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Shrink
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 194
type: SurveyResponse
date created: 2025-12-02T17:55:16.880
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Analytics / BI, ML / AI
storageenvironment: Mixed/hybrid
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Data modeling, Governance / quality checks
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poorly designed source applications and databases, combined with a lack of clear data ownership
teamgrowth2026: Not sure
educationtopic: AI/LLM integration
region: Australia / New Zealand

survey entry 195
type: SurveyResponse
date created: 2025-12-02T17:56:15.666
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation, Infrastructure / platform work, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: GitHub Actions
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 196
type: SurveyResponse
date created: 2025-12-02T18:18:49.270
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Architecture patterns
region: Europe (EU / UK)

survey entry 197
type: SurveyResponse
date created: 2025-12-02T18:21:03.743
industry: Tech
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Data Architect
orgsize: 200–999
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: AI/LLM integration
industrywish: I think your focus on data modeling is exactly right as I see a lack of skills and focus on data modeling to be one of the TWO big issues (quality being the other .. not data quality but quality in the Robert Pirsig sense)
region: Australia / New Zealand

survey entry 198
type: SurveyResponse
date created: 2025-12-02T18:28:16.755
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation, Infrastructure / platform work
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Data modeling, Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Career growth / leadership
region: Asia–Pacific

survey entry 199
type: SurveyResponse
date created: 2025-12-02T18:32:32.840
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: 200–999
teamfocus: Data modeling / transformation, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 200
type: SurveyResponse
date created: 2025-12-02T18:40:43.657
industry: Tech
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Architect
orgsize: 10,000+
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling, AI tools produce inconsistent schemas
architecturetrend: Centralized warehouse
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: Asia–Pacific

survey entry 201
type: SurveyResponse
date created: 2025-12-02T19:03:43.968
industry: Retail/e-commerce
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: < 50 employees
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Mixed/hybrid
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Not sure
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 202
type: SurveyResponse
date created: 2025-12-02T19:34:07.381
industry: Healthcare
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 50–199
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Fivetran transformations
aiusagefrequency: Multiple times per day
aihelpswith: Pipeline debugging, Documentation / data discovery
aiadoption: AI embedded in most workflows
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
industrywish: It’s not just software engineering 10 years ago but it has its own challenges that software engineers don’t need to worry about.
region: United States / Canada

survey entry 203
type: SurveyResponse
date created: 2025-12-02T19:44:35.380
industry: Finance
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Mixed/hybrid
orchestration: Matillion
aiusagefrequency: Rarely
aihelpswith: Governance / quality checks
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: Career growth / leadership
industrywish: That it takes time to build data models that are fit for business, and these don’t just happen overnight or within a “sprint”.
region: United States / Canada

survey entry 204
type: SurveyResponse
date created: 2025-12-02T19:47:23.689
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 200–999
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: On-prem data warehouse
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Data modeling
aiadoption: Using AI for tactical tasks
modelingapproach: Raw layer - event driven/canonical, datamart level kimball/add-hoc
modelingpainpoints: Pressure to “move fast”
architecturetrend: modern dimensional warehouse / analytics engineering
biggestbottleneck: Org model
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
industrywish: Meanings
region: Europe (EU / UK)

survey entry 205
type: SurveyResponse
date created: 2025-12-02T19:52:40.077
industry: Public sector / Education
job title: Software Engineer working on data
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Software Engineer working on data
orgsize: 10,000+
teamfocus: Analytics / BI, ML / AI
storageenvironment: Cloud PostgreSQL/MySQL
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Shrink
educationtopic: Data modeling
region: United States / Canada

survey entry 206
type: SurveyResponse
date created: 2025-12-02T19:56:16.147
industry: Manufacturing / Industrial
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: < 50 employees
teamfocus: Data modeling / transformation, Data quality / reliability, Analytics / BI
storageenvironment: Cloud PostgreSQL/MySQL
orchestration: Dagster
aiusagefrequency: Daily
aihelpswith: Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: AI/LLM integration
industrywish: Business impact and can-do attitude matter a lot more than many junior people realize. Data is a nexus in most organizations and DEs / Analysts / DSs / Etc. often have highly strategic "crow's nest" vantage points yet fail to capitalize on this opportunity.
region: United States / Canada

survey entry 207
type: SurveyResponse
date created: 2025-12-02T20:11:10.042
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data quality / reliability
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: AI/LLM integration
industrywish: Prioritising value of Data first rather than Data modernization
region: Asia–Pacific

survey entry 208
type: SurveyResponse
date created: 2025-12-02T20:13:08.646
industry: Tech
job title: Migration Consultant
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Migration Consultant
orgsize: < 50 employees
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: On-prem data warehouse
orchestration: Value-Centric
aiusagefrequency: Rarely
aihelpswith: Documentation / data discovery
aiadoption: No meaningful adoption yet
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership
architecturetrend: Event-driven architecture
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
industrywish: It's not a project. It's a program that needs to be treated as a capital investment.
region: United States / Canada

survey entry 209
type: SurveyResponse
date created: 2025-12-02T20:17:43.313
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data quality / reliability
storageenvironment: Mixed/hybrid
orchestration: Airflow
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Tools don’t support good modeling
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Streaming / event-driven systems
region: United States / Canada

survey entry 210
type: SurveyResponse
date created: 2025-12-02T20:30:20.153
industry: Manufacturing / Industrial
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 10,000+
teamfocus: Fighting fires
storageenvironment: Mixed/hybrid
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Documentation / data discovery, Data modeling
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Lack of leadership and talent, tech debt
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: Importance of data foundations
region: United States / Canada

survey entry 211
type: SurveyResponse
date created: 2025-12-02T20:36:37.291
industry: Retail/e-commerce
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Analytics Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Analytics / BI
storageenvironment: On-prem data warehouse
orchestration: CRON and SSIS
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: That is is where the magic (data) is created
region: Australia / New Zealand

survey entry 212
type: SurveyResponse
date created: 2025-12-02T20:45:23.491
industry: Tech
job title: Report Developer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Report Developer
orgsize: 10,000+
teamfocus: Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Shrink
educationtopic: Architecture patterns
region: Asia–Pacific

survey entry 213
type: SurveyResponse
date created: 2025-12-02T21:00:51.006
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Data quality / reliability, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Architecture design
aiadoption: Experimenting
modelingapproach: Data Vault
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 214
type: SurveyResponse
date created: 2025-12-02T21:14:14.711
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Data quality / reliability, Infrastructure / platform work
storageenvironment: Iceberg on S3 w/ Glue & Lake Formation, hand-rolled data platform.
orchestration: Argo
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Data modeling
industrywish: DE is a SWE discipline and not enforcing good discipline gets you in trouble.
region: United States / Canada

survey entry 215
type: SurveyResponse
date created: 2025-12-02T21:17:13.780
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: < 50 employees
teamfocus: Data modeling / transformation, Infrastructure / platform work, ML / AI
storageenvironment: Cloud PostgreSQL/MySQL
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Pressure to “move fast”
architecturetrend: Event-driven architecture
biggestbottleneck: Talent / hiring
teamgrowth2026: Not sure
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 216
type: SurveyResponse
date created: 2025-12-02T21:33:03.169
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: AI embedded in most workflows
modelingapproach: One Big Table
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: Australia / New Zealand

survey entry 217
type: SurveyResponse
date created: 2025-12-02T21:36:28.408
industry: Finance
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Data Vault
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 218
type: SurveyResponse
date created: 2025-12-02T21:45:02.889
industry: Tech
job title: Data Products
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Products
orgsize: 10,000+
teamfocus: Data quality / reliability, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: There is no one size all fit approach, and a lot technical debt to get to innovation
region: United States / Canada

survey entry 219
type: SurveyResponse
date created: 2025-12-02T22:12:21.635
industry: Retail/e-commerce
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data quality / reliability, Infrastructure / platform work, Analytics / BI, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Architecture design
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Ways of working / offshoring
teamgrowth2026: Grow
educationtopic: Architecture patterns
industrywish: Large enterprises are bound by their legacy applications which constrain data engineering initiatives. This is further compounded by the ways of working (off shoring), making it very difficult to adopt industry best practices, largely because decisions are often framed with how to make hybrid architectures work ( old and new platforms). Both states often need to coexist for longer timeframes than we would like to admit
region: Europe (EU / UK)

survey entry 220
type: SurveyResponse
date created: 2025-12-02T22:21:22.051
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Autosys
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery
aiadoption: No meaningful adoption yet
modelingapproach: Data Vault
modelingpainpoints: Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Shrink
educationtopic: Architecture patterns
region: Asia–Pacific

survey entry 221
type: SurveyResponse
date created: 2025-12-02T22:27:15.640
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Middle East Africa
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Mixed/hybrid
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Streaming / event-driven systems
industrywish: Data is simply the backend for the backend
region: Middle East / Africa

survey entry 222
type: SurveyResponse
date created: 2025-12-02T22:27:53.706
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Middle East Africa
role: Data Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: AI embedded in most workflows
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Compute costs
teamgrowth2026: Stay the same
educationtopic: Streaming / event-driven systems
region: Middle East / Africa

survey entry 223
type: SurveyResponse
date created: 2025-12-02T22:36:56.689
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 200–999
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Data quality
teamgrowth2026: Grow
educationtopic: Data modeling
industrywish: The way we spend our working days is the way we spend our working lives
region: Europe (EU / UK)

survey entry 224
type: SurveyResponse
date created: 2025-12-02T22:47:57.909
industry: Retail/e-commerce
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 200–999
teamfocus: Data modeling / transformation, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Dagster
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Tools don’t support good modeling
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: AI/LLM integration
industrywish: Data quality start left
region: Europe (EU / UK)

survey entry 225
type: SurveyResponse
date created: 2025-12-02T23:12:21.584
industry: Manufacturing / Industrial
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Architecture design, Governance / quality checks
aiadoption: Using AI for tactical tasks
modelingapproach: Canonical/semantic models
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: Europe (EU / UK)

survey entry 226
type: SurveyResponse
date created: 2025-12-02T23:18:41.667
industry: Public sector / Education
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 200–999
teamfocus: Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Pipeline debugging, Architecture design
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Centralized warehouse
biggestbottleneck: Tool complexity
teamgrowth2026: Grow
educationtopic: Data modeling
region: United States / Canada

survey entry 227
type: SurveyResponse
date created: 2025-12-02T23:25:35.530
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: < 50 employees
teamfocus: Data modeling / transformation, Infrastructure / platform work
storageenvironment: RDF Graphs
orchestration: GitLab CI
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery, Data modeling
aiadoption: Using AI for tactical tasks
modelingapproach: Knowledge Graph/semantic
modelingpainpoints: None / modeling is going well
architecturetrend: Events in KGs
biggestbottleneck: none
teamgrowth2026: Grow
educationtopic: we train others ;)
industrywish: Everything is a graph
region: Europe (EU / UK)

survey entry 228
type: SurveyResponse
date created: 2025-12-02T23:27:40.833
industry: Healthcare
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: S3
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Data modeling
aiadoption: No meaningful adoption yet
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Medallion
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Not sure
educationtopic: Architecture patterns
region: Europe (EU / UK)

survey entry 229
type: SurveyResponse
date created: 2025-12-02T23:33:07.053
industry: Finance
job title: BI Functional analyst
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: BI Functional analyst
orgsize: 50–199
teamfocus: Analytics / BI, Fighting fires
storageenvironment: SAS cloud
orchestration: No orchestration / ad-hoc
aiusagefrequency: Rarely
aihelpswith: Pipeline debugging, Documentation / data discovery
aiadoption: No meaningful adoption yet
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Stay the same
educationtopic: Data modeling
industrywish: Mack of data modeling expertise will turn your powerful data infra into a giant mess impossible to maintain, with sky-high change costs.
region: Europe (EU / UK)

survey entry 230
type: SurveyResponse
date created: 2025-12-02T23:47:04.108
industry: Manufacturing / Industrial
job title: Data analyst
sic: this
naics: this
occupational category: this
spatial coverage: Middle East Africa
role: Data analyst
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: File/power Query
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Data modeling
aiadoption: No meaningful adoption yet
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: Middle East / Africa

survey entry 231
type: SurveyResponse
date created: 2025-12-02T23:51:01.264
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data modeling / transformation, Infrastructure / platform work, Fighting fires
storageenvironment: Mixed/hybrid
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Data Vault
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: AI/LLM integration
industrywish: It is complex, touches multiple disciplines
region: Europe (EU / UK)

survey entry 232
type: SurveyResponse
date created: 2025-12-02T23:55:07.355
industry: Manufacturing / Industrial
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Analytics / BI, ML / AI
storageenvironment: On-prem data warehouse
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Building internal AI platforms
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Tool complexity
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: Europe (EU / UK)

survey entry 233
type: SurveyResponse
date created: 2025-12-03T00:07:33.627
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Tools don’t support good modeling
architecturetrend: Lakehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Shrink
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 234
type: SurveyResponse
date created: 2025-12-03T00:13:14.752
industry: Healthcare
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 200–999
teamfocus: Data quality / reliability, Analytics / BI
storageenvironment: Cloud Azure SQL DB
orchestration: Azure Data Factory
aiusagefrequency: Rarely
aihelpswith: I don't find AI helpful
aiadoption: No meaningful adoption yet
modelingapproach: Data Vault (Data Warehouse) and Kimball (Data Marts)
modelingpainpoints: None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: That data engineers (or IT people) don't 'own' data and that data (management) is not IT.
region: Europe (EU / UK)

survey entry 235
type: SurveyResponse
date created: 2025-12-03T00:14:50.403
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability
storageenvironment: On-prem data warehouse
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: United States / Canada

survey entry 236
type: SurveyResponse
date created: 2025-12-03T00:29:05.517
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Architecture patterns
industrywish: How much time and effort it takes. The complexity behind it.
region: Europe (EU / UK)

survey entry 237
type: SurveyResponse
date created: 2025-12-03T00:55:04.968
industry: Retail/e-commerce
job title: Data PM
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data PM
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: Data Vault
modelingpainpoints: None / modeling is going well
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 238
type: SurveyResponse
date created: 2025-12-03T01:04:13.746
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Engineer
orgsize: 200–999
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud PostgreSQL/MySQL
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: AI embedded in most workflows
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: Asia–Pacific

survey entry 239
type: SurveyResponse
date created: 2025-12-03T01:16:07.935
industry: Tech
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 50–199
teamfocus: Data modeling / transformation, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Data Factory & such on MS-stack.
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Tool complexity
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: Europe (EU / UK)

survey entry 240
type: SurveyResponse
date created: 2025-12-03T01:29:56.673
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Data Engineer
orgsize: 50–199
teamfocus: Data modeling / transformation, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
industrywish: The skills involved, software engineering requirements for tooling and platforms.
region: Australia / New Zealand

survey entry 241
type: SurveyResponse
date created: 2025-12-03T01:36:05.847
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: One Big Table
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: Europe (EU / UK)

survey entry 242
type: SurveyResponse
date created: 2025-12-03T01:41:59.291
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 10,000+
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Building internal AI platforms
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Tool complexity
teamgrowth2026: Grow
educationtopic: Career growth / leadership
region: Europe (EU / UK)

survey entry 243
type: SurveyResponse
date created: 2025-12-03T01:49:06.388
industry: Finance
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Fighting fires
storageenvironment: On-prem data warehouse
orchestration: No orchestration / ad-hoc
aiusagefrequency: Rarely
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
industrywish: domain knowleedge is key and it must be mandatory part of data projects
region: Europe (EU / UK)

survey entry 244
type: SurveyResponse
date created: 2025-12-03T02:05:03.410
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines, Analytics / BI, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Dbt
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Not sure
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 245
type: SurveyResponse
date created: 2025-12-03T02:07:18.926
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Middle East Africa
role: Data Engineer
orgsize: < 50 employees
teamfocus: Ingestion / pipelines
storageenvironment: On-prem data warehouse
orchestration: Prefect
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: No meaningful adoption yet
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: AI tools produce inconsistent schemas
architecturetrend: Centralized warehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Not sure
educationtopic: Data modeling
industrywish: That we shouldn't do the job of 3 roles/titles while we should maintain the pipelines and the data quality
region: Middle East / Africa

survey entry 246
type: SurveyResponse
date created: 2025-12-03T02:27:27.437
industry: Healthcare
job title: AI Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: AI Engineer
orgsize: < 50 employees
teamfocus: Data quality / reliability, Infrastructure / platform work, ML / AI
storageenvironment: Mixed/hybrid
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery
aiadoption: No meaningful adoption yet
modelingapproach: Canonical/semantic models
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: That it’s logic
region: Asia–Pacific

survey entry 247
type: SurveyResponse
date created: 2025-12-03T02:56:28.354
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: On-prem data warehouse
orchestration: Talend
aiusagefrequency: Rarely
aihelpswith: I don't find AI helpful
aiadoption: No meaningful adoption yet
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: That in practice it is never about how many tools one knows and how shiny the architecture is, but knowing the business and being able to communicate with the stakeholders on their own language (sometimes this literaly means using their vocabulary). This was the main pillar in dimensional modeling and except for the cloud, I don't really see the industry has moved much since it was introduced in 1996.
region: Europe (EU / UK)

survey entry 248
type: SurveyResponse
date created: 2025-12-03T03:05:27.014
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Analytics / BI, Fighting fires
storageenvironment: On-prem data warehouse
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Data modeling
aiadoption: Using AI for tactical tasks
modelingapproach: One Big Table
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Shrink
educationtopic: Career growth / leadership
industrywish: Data quality can't be compronised even during urgency
region: Asia–Pacific

survey entry 249
type: SurveyResponse
date created: 2025-12-03T03:24:44.363
industry: Public sector / Education
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: ML / AI, Fighting fires
storageenvironment: Mixed/hybrid
orchestration: Dagster
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Event-driven architecture
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Streaming / event-driven systems
region: Europe (EU / UK)

survey entry 250
type: SurveyResponse
date created: 2025-12-03T03:55:01.414
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 200–999
teamfocus: Data modeling / transformation, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Career growth / leadership
industrywish: Writing SQL is easy. Writing the correct SQL is hard.
region: Europe (EU / UK)

survey entry 251
type: SurveyResponse
date created: 2025-12-03T04:03:02.328
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Data modeling
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 252
type: SurveyResponse
date created: 2025-12-03T04:09:21.568
industry: Finance
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Middle East Africa
role: Analytics Engineer
orgsize: 200–999
teamfocus: Data quality / reliability, Analytics / BI, Fighting fires
storageenvironment: Mixed/hybrid
orchestration: No orchestration / ad-hoc
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Data modeling, Architecture design
aiadoption: No meaningful adoption yet
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Data modeling
industrywish: I wish that data engineers and business stakeholders understand that both need to Walk on other shoes and give each other as much information as possible.
region: Middle East / Africa

survey entry 253
type: SurveyResponse
date created: 2025-12-03T04:39:24.531
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Data Vault
modelingpainpoints: AI tools produce inconsistent schemas, None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: Europe (EU / UK)

survey entry 254
type: SurveyResponse
date created: 2025-12-03T04:44:26.872
industry: Healthcare
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Data Architect
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation, Infrastructure / platform work, Analytics / BI, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Data factory
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Data Vault
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”, AI tools produce inconsistent schemas
architecturetrend: Lakehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: Australia / New Zealand

survey entry 255
type: SurveyResponse
date created: 2025-12-03T05:12:18.692
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: On-prem data warehouse
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Data modeling
industrywish: Data governance isn’t only data engineers responsibility
region: United States / Canada

survey entry 256
type: SurveyResponse
date created: 2025-12-03T05:33:17.181
industry: Manufacturing / Industrial
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Architect
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Home grown app over 20 years old
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Data modeling
industrywish: The "shift left" mindset that drives TDD in systems development applies just as much if not more in data; no amount of data engineering magic will fix poor data quality / definition / constraints at the sources.
region: United States / Canada

survey entry 257
type: SurveyResponse
date created: 2025-12-03T06:05:57.555
industry: Tech
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Architect
orgsize: 10,000+
teamfocus: Data modeling / transformation, Data quality / reliability, ML / AI
storageenvironment: Mixed/hybrid
orchestration: Airflow and Dagster
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Data modeling, Architecture design, Governance / quality checks
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”, AI tools produce inconsistent schemas
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Data modeling
region: Asia–Pacific

survey entry 258
type: SurveyResponse
date created: 2025-12-03T06:06:19.458
industry: Tech
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Architect
orgsize: < 50 employees
teamfocus: Data modeling / transformation, Data quality / reliability, Infrastructure / platform work
storageenvironment: Mixed/hybrid
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Canonical/semantic models
modelingpainpoints: Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Compute costs
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 259
type: SurveyResponse
date created: 2025-12-03T06:24:05.476
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: < 50 employees
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: On-prem data warehouse
orchestration: Dagster
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: No meaningful adoption yet
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 260
type: SurveyResponse
date created: 2025-12-03T06:41:46.875
industry: Finance
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Data quality / reliability, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Data modeling, Architecture design, Governance / quality checks
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: Streaming / event-driven systems
region: Latin America

survey entry 261
type: SurveyResponse
date created: 2025-12-03T06:48:28.763
industry: Public sector / Education
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Engineer
orgsize: 200–999
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: how to elevate data literacy of other employees
industrywish: how foundational and important it is for the rest. give proper visibility to DE
region: Asia–Pacific

survey entry 262
type: SurveyResponse
date created: 2025-12-03T07:00:05.163
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 50–199
teamfocus: Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Architecture design
aiadoption: AI embedded in most workflows
modelingapproach: Canonical/semantic models
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Tools don’t support good modeling
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 263
type: SurveyResponse
date created: 2025-12-03T07:11:42.235
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: < 50 employees
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Streaming / event-driven systems
region: United States / Canada

survey entry 264
type: SurveyResponse
date created: 2025-12-03T08:18:37.628
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: 200–999
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Weekly
aihelpswith: Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on author)
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Planning to do more than we end up having time for. So IDK, maybe talent?
teamgrowth2026: Stay the same
educationtopic: Statistics
industrywish: It’s better when it doesn’t break, even if it’s less visible than fixing failures
region: United States / Canada

survey entry 265
type: SurveyResponse
date created: 2025-12-03T08:22:05.142
industry: Public sector / Education
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Manager / Director / VP
orgsize: 10,000+
teamfocus: Data quality / reliability, Infrastructure / platform work
storageenvironment: On-prem data warehouse
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: Asia–Pacific

survey entry 266
type: SurveyResponse
date created: 2025-12-03T09:05:18.563
industry: Public sector / Education
job title: Software Engineer working on data
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Software Engineer working on data
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Analytics / BI
storageenvironment: On-prem data warehouse
orchestration: Custom Tool
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Reliability engineering
region: United States / Canada

survey entry 267
type: SurveyResponse
date created: 2025-12-03T09:13:11.237
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Experimenting
modelingapproach: Data Vault
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Compute costs
teamgrowth2026: Stay the same
educationtopic: Career growth / leadership
industrywish: i wish it gets same exposure as software engineering, bcoz if you look at it carefully the data we provide is the one that runs or breaks the company.
region: United States / Canada

survey entry 268
type: SurveyResponse
date created: 2025-12-03T09:19:24.452
industry: Healthcare
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: No meaningful adoption yet
modelingapproach: One Big Table
modelingpainpoints: Lack of clear ownership
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 269
type: SurveyResponse
date created: 2025-12-03T10:33:08.267
industry: Finance
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Dagster
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Event-driven architecture
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Streaming / event-driven systems
industrywish: It's much more important than model development.
region: United States / Canada

survey entry 270
type: SurveyResponse
date created: 2025-12-03T11:45:23.378
industry: Finance
job title: Data Scientist
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Scientist
orgsize: 1,000–10,000
teamfocus: ML / AI, Fighting fires
storageenvironment: On-prem data warehouse
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Building internal AI platforms
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: None / modeling is going well
architecturetrend: Lakehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: Europe (EU / UK)

survey entry 271
type: SurveyResponse
date created: 2025-12-03T11:50:13.823
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: < 50 employees
teamfocus: Analytics / BI, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: It depends
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery
aiadoption: No meaningful adoption yet
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: The value it can bring
region: Europe (EU / UK)

survey entry 272
type: SurveyResponse
date created: 2025-12-03T12:08:50.954
industry: Manufacturing / Industrial
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 10,000+
teamfocus: Data modeling / transformation, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 273
type: SurveyResponse
date created: 2025-12-03T12:49:36.972
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Fabric
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Data modeling, Architecture design, Governance / quality checks
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 274
type: SurveyResponse
date created: 2025-12-03T12:52:52.833
industry: Public sector / Education
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Prefect
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Governance / quality checks
aiadoption: AI embedded in most workflows
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 275
type: SurveyResponse
date created: 2025-12-03T13:44:36.262
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 200–999
teamfocus: Data modeling / transformation, Infrastructure / platform work
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Governance / quality checks
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Data modeling
region: United States / Canada

survey entry 276
type: SurveyResponse
date created: 2025-12-03T13:57:13.510
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: We're not order takers, but rather developers who should be incentivized to build great products people love.
region: United States / Canada

survey entry 277
type: SurveyResponse
date created: 2025-12-03T14:03:00.262
industry: Tech
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: < 50 employees
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Data modeling
aiadoption: Using AI for tactical tasks
modelingapproach: Canonical/semantic models
modelingpainpoints: Lack of clear ownership
architecturetrend: Centralized warehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Stay the same
educationtopic: Streaming / event-driven systems
industrywish: Primary goal is to help business end-user, not technology adoption.
region: Europe (EU / UK)

survey entry 278
type: SurveyResponse
date created: 2025-12-03T14:06:53.480
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 200–999
teamfocus: Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: Europe (EU / UK)

survey entry 279
type: SurveyResponse
date created: 2025-12-03T14:10:36.110
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data modeling / transformation, Infrastructure / platform work, Analytics / BI, ML / AI
storageenvironment: Cloud PostgreSQL/MySQL
orchestration: Mage
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Canonical/semantic models
modelingpainpoints: None / modeling is going well
architecturetrend: Lakehouse
biggestbottleneck: Sales
teamgrowth2026: Shrink
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 280
type: SurveyResponse
date created: 2025-12-03T14:36:49.530
industry: Public sector / Education
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Fighting fires
storageenvironment: On-prem data warehouse
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Pipeline debugging, Architecture design
aiadoption: Experimenting
modelingapproach: Data Vault
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 281
type: SurveyResponse
date created: 2025-12-03T14:56:17.842
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 200–999
teamfocus: Data modeling / transformation, Data quality / reliability, Infrastructure / platform work
storageenvironment: Mixed/hybrid
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: Ai chat with data
region: Europe (EU / UK)

survey entry 282
type: SurveyResponse
date created: 2025-12-03T16:12:00.931
industry: Retail/e-commerce
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Architect
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 283
type: SurveyResponse
date created: 2025-12-03T17:01:56.957
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Rarely
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: No meaningful adoption yet
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
industrywish: Important of data modeling
region: United States / Canada

survey entry 284
type: SurveyResponse
date created: 2025-12-03T17:49:11.309
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines, Data quality / reliability
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Weekly
aihelpswith: Documentation / data discovery
aiadoption: Experimenting
modelingapproach: One Big Table
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Data modeling
region: United States / Canada

survey entry 285
type: SurveyResponse
date created: 2025-12-03T18:10:10.133
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Fighting fires
storageenvironment: Cloud PostgreSQL/MySQL
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Architecture design
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Event-driven architecture
biggestbottleneck: Ingest quality
teamgrowth2026: Grow
educationtopic: Architecture patterns
industrywish: It always takes longer than you think
region: United States / Canada

survey entry 286
type: SurveyResponse
date created: 2025-12-03T19:25:40.006
industry: Public sector / Education
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 200–999
teamfocus: Data quality / reliability, Fighting fires
storageenvironment: Mixed/hybrid
orchestration: qlik talend
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: the importance of good data because of the pain it causes.. understanding the dataa is the proff that you did job correctly because its the trail..
region: United States / Canada

survey entry 287
type: SurveyResponse
date created: 2025-12-03T20:18:27.068
industry: Tech
job title: Software Engineer working on data
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Software Engineer working on data
orgsize: 50–199
teamfocus: Data modeling / transformation, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Custom application
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Tools don’t support good modeling
architecturetrend: Centralized warehouse
biggestbottleneck: Data quality
teamgrowth2026: Grow
educationtopic: Data modeling
region: United States / Canada

survey entry 288
type: SurveyResponse
date created: 2025-12-03T21:04:29.369
industry: Public sector / Education
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Rarely
aihelpswith: Documentation / data discovery
aiadoption: No meaningful adoption yet
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Architecture patterns
industrywish: Data has to be driven from a shared business roadmap, have an isolated view creates isolated data
region: Australia / New Zealand

survey entry 289
type: SurveyResponse
date created: 2025-12-03T22:12:43.432
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, ML / AI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Control-M :(
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Event-driven modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Data quality
teamgrowth2026: Shrink
educationtopic: Career growth / leadership
region: Australia / New Zealand

survey entry 290
type: SurveyResponse
date created: 2025-12-04T00:19:46.050
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 10,000+
teamfocus: Data modeling / transformation, Infrastructure / platform work
storageenvironment: On-prem data warehouse
orchestration: No orchestration / ad-hoc
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time
architecturetrend: Centralized warehouse
biggestbottleneck: Tool complexity
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 291
type: SurveyResponse
date created: 2025-12-04T01:02:46.423
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Mixed/hybrid
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: It depends
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Shrink
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: That AI / LLM / Agents do not solve magically the need for data / software engineering best practices
region: Europe (EU / UK)

survey entry 292
type: SurveyResponse
date created: 2025-12-04T01:11:40.053
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: own
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Streaming / event-driven systems
region: United States / Canada

survey entry 293
type: SurveyResponse
date created: 2025-12-04T01:51:25.069
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling
architecturetrend: Centralized warehouse
biggestbottleneck: Tool complexity
teamgrowth2026: Grow
educationtopic: Architecture patterns
region: Australia / New Zealand

survey entry 294
type: SurveyResponse
date created: 2025-12-04T02:53:40.322
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 200–999
teamfocus: Infrastructure / platform work, Analytics / BI
storageenvironment: On-prem data warehouse
orchestration: Dagster
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 295
type: SurveyResponse
date created: 2025-12-04T03:17:10.530
industry: Tech
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Architect
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data quality / reliability, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Architecture design, Governance / quality checks
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Data modeling
region: Asia–Pacific

survey entry 296
type: SurveyResponse
date created: 2025-12-04T05:19:20.149
industry: Healthcare
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: Data Engineer
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: own platform
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Data modeling, Architecture design
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Data quality
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: Latin America

survey entry 297
type: SurveyResponse
date created: 2025-12-04T05:27:42.814
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Engineer
orgsize: 200–999
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: On-prem data warehouse
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Data modeling
aiadoption: Using AI for tactical tasks
modelingapproach: Event-driven modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Event-driven architecture
biggestbottleneck: Data quality
teamgrowth2026: Grow
educationtopic: Data modeling
region: Asia–Pacific

survey entry 298
type: SurveyResponse
date created: 2025-12-04T06:10:22.565
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Infrastructure / platform work, Fighting fires
storageenvironment: Mixed/hybrid
orchestration: Cron on EC2
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
industrywish: How critical it is for anything to do with data.
region: Australia / New Zealand

survey entry 299
type: SurveyResponse
date created: 2025-12-04T06:17:07.961
industry: Tech
job title: Software Engineer working on data
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Software Engineer working on data
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Compute costs
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
industrywish: Adopt a more functional / declarative style approach in the tools being built
region: Europe (EU / UK)

survey entry 300
type: SurveyResponse
date created: 2025-12-04T06:53:32.378
industry: Public sector / Education
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Analytics / BI
storageenvironment: Mixed/hybrid
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Reliability engineering
industrywish: Better coaching and knowledge transfer from the seniors to the newbies is very important.
region: United States / Canada

survey entry 301
type: SurveyResponse
date created: 2025-12-04T07:39:40.664
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Dagster
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
industrywish: Build a data team does not magically tell the industry what best to do/ which data to produce or collect.
region: Asia–Pacific

survey entry 302
type: SurveyResponse
date created: 2025-12-04T08:10:18.427
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: Data Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines, Data modeling / transformation, Infrastructure / platform work
storageenvironment: Cloud PostgreSQL/MySQL
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: None / modeling is going well
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Architecture patterns
region: Latin America

survey entry 303
type: SurveyResponse
date created: 2025-12-04T09:04:13.985
industry: Public sector / Education
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: Analytics Engineer
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: Latin America

survey entry 304
type: SurveyResponse
date created: 2025-12-04T09:37:20.084
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Dagster
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership
architecturetrend: Centralized warehouse
biggestbottleneck: Compute costs
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: Asia–Pacific

survey entry 305
type: SurveyResponse
date created: 2025-12-04T09:46:03.519
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 10,000+
teamfocus: Data modeling / transformation, Data quality / reliability, Infrastructure / platform work
storageenvironment: Mixed/hybrid
orchestration: Watson Orchestrate, Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Event-driven architecture
biggestbottleneck: Data quality
teamgrowth2026: Shrink
educationtopic: AI/LLM integration
industrywish: Clear understanding of business and technical requirements.
region: United States / Canada

survey entry 306
type: SurveyResponse
date created: 2025-12-04T10:32:03.360
industry: Healthcare
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: < 50 employees
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 307
type: SurveyResponse
date created: 2025-12-04T10:32:17.979
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data modeling / transformation, ML / AI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: None / modeling is going well
architecturetrend: Lakehouse
biggestbottleneck: Compute costs
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: United States / Canada

survey entry 308
type: SurveyResponse
date created: 2025-12-04T10:33:07.030
industry: Manufacturing / Industrial
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 10,000+
teamfocus: Analytics / BI, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: cron
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: No meaningful adoption yet
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Shrink
educationtopic: Reliability engineering
region: Europe (EU / UK)

survey entry 309
type: SurveyResponse
date created: 2025-12-04T10:35:06.351
industry: Public sector / Education
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 200–999
teamfocus: Data modeling / transformation
storageenvironment: Mixed/hybrid
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery
aiadoption: AI embedded in most workflows
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Shrink
educationtopic: Reliability engineering
region: Europe (EU / UK)

survey entry 310
type: SurveyResponse
date created: 2025-12-04T10:37:26.846
industry: Tech
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Architect
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Talend Cloud
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Building internal AI platforms
modelingapproach: Data Vault
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Virtual Warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Data modeling
industrywish: Hire & Listen to Experts
region: United States / Canada

survey entry 311
type: SurveyResponse
date created: 2025-12-04T10:37:38.825
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 50–199
teamfocus: Data modeling / transformation, ML / AI
storageenvironment: snowflake + iceberg w/ glue
orchestration: argo workflows
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Governance / quality checks
aiadoption: AI embedded in most workflows
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 312
type: SurveyResponse
date created: 2025-12-04T10:38:47.490
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation, Analytics / BI, ML / AI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Rarely
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Building internal AI platforms
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: United States / Canada

survey entry 313
type: SurveyResponse
date created: 2025-12-04T10:39:08.863
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Fighting fires
storageenvironment: Mixed/hybrid
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Shrink
educationtopic: Architecture patterns
region: Europe (EU / UK)

survey entry 314
type: SurveyResponse
date created: 2025-12-04T10:39:25.084
industry: Public sector / Education
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: < 50 employees
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Dagster
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: AI embedded in most workflows
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 315
type: SurveyResponse
date created: 2025-12-04T10:39:46.203
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Infrastructure / platform work, Fighting fires
storageenvironment: S3 (parquet, csv, whatever) + Amazon Athena
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery, Architecture design
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: "startup culture"
teamgrowth2026: Shrink
educationtopic: Career growth / leadership
region: United States / Canada

survey entry 316
type: SurveyResponse
date created: 2025-12-04T10:39:57.083
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 200–999
teamfocus: Infrastructure / platform work, Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Architecture patterns
region: Europe (EU / UK)

survey entry 317
type: SurveyResponse
date created: 2025-12-04T10:40:59.479
industry: Retail/e-commerce
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 200–999
teamfocus: Data modeling / transformation, Data quality / reliability, Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: One Big Table
modelingpainpoints: Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Data quality
teamgrowth2026: Shrink
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 318
type: SurveyResponse
date created: 2025-12-04T10:41:26.499
industry: Public sector / Education
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines, Data modeling / transformation, Infrastructure / platform work, Analytics / BI, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Data modeling, Architecture design, Governance / quality checks
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 319
type: SurveyResponse
date created: 2025-12-04T10:43:47.583
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 200–999
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Mixed/hybrid
orchestration: No orchestration / ad-hoc
aiusagefrequency: Weekly
aihelpswith: Pipeline debugging, Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
industrywish: How valuable DE is and how - in the age of information - it can basically make or break a company. Arguably most important/impactful org in a company
region: United States / Canada

survey entry 320
type: SurveyResponse
date created: 2025-12-04T10:44:39.760
industry: Finance
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Architect
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Analytics / BI, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Dagster
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Architecture design, Governance / quality checks
aiadoption: AI embedded in most workflows
modelingapproach: Data Vault
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Tools don’t support good modeling
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 321
type: SurveyResponse
date created: 2025-12-04T10:44:44.433
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Data modeling
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 322
type: SurveyResponse
date created: 2025-12-04T10:45:04.745
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data quality / reliability, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Governance / quality checks
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: vendors currently build "own" semantics layer into their plattforms. They at least should provide an interface to ingest and maintain "own Knowledge" (own KG, Ontologies) into their proprieatary semantic tier.
region: Europe (EU / UK)

survey entry 323
type: SurveyResponse
date created: 2025-12-04T10:45:29.931
industry: Healthcare
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Analytics Engineer
orgsize: 50–199
teamfocus: Data modeling / transformation, Data quality / reliability, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Github Actions
aiusagefrequency: Rarely
aihelpswith: Pipeline debugging
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: Asia–Pacific

survey entry 324
type: SurveyResponse
date created: 2025-12-04T10:46:22.798
industry: Manufacturing / Industrial
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: AI/LLM integration
industrywish: If the underlying data/systems are poor then no engineering effort can 100% fix that. Invest in the foundations.
region: Europe (EU / UK)

survey entry 325
type: SurveyResponse
date created: 2025-12-04T10:50:32.144
industry: Finance
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: alteryx
aiusagefrequency: Never
aihelpswith: I don't find AI helpful
aiadoption: No meaningful adoption yet
modelingapproach: 1 table per report
modelingpainpoints: Tools don’t support good modeling
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: United States / Canada

survey entry 326
type: SurveyResponse
date created: 2025-12-04T10:52:54.506
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Streaming / event-driven systems
industrywish: Without engineering, all of the data people clamor over would be utterly unusable and unreliable.
region: United States / Canada

survey entry 327
type: SurveyResponse
date created: 2025-12-04T10:55:31.231
industry: Retail/e-commerce
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: orchestra
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Talent / hiring
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 328
type: SurveyResponse
date created: 2025-12-04T10:57:11.616
industry: Tech
job title: Platform Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Platform Engineer
orgsize: 10,000+
teamfocus: Infrastructure / platform work
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Event-driven modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Event-driven architecture
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Streaming / event-driven systems
region: Europe (EU / UK)

survey entry 329
type: SurveyResponse
date created: 2025-12-04T10:58:45.076
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 50–199
teamfocus: Infrastructure / platform work, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: AI embedded in most workflows
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Shrink
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 330
type: SurveyResponse
date created: 2025-12-04T10:59:26.008
industry: Public sector / Education
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 10,000+
teamfocus: Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 331
type: SurveyResponse
date created: 2025-12-04T10:59:43.618
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 200–999
teamfocus: Data modeling / transformation, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Building internal AI platforms
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Architecture patterns
region: United States / Canada

survey entry 332
type: SurveyResponse
date created: 2025-12-04T11:05:37.542
industry: Manufacturing / Industrial
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Mixed/hybrid
orchestration: Matillion and SSIS(yes believe it)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
industrywish: Challenges of Complexity.
region: United States / Canada

survey entry 333
type: SurveyResponse
date created: 2025-12-04T11:11:52.691
industry: Tech
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Architect
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Canonical/semantic models
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Architecture patterns
industrywish: Good + Fast → Not Cheap (it'll cost a lot) Fast + Cheap → Not Good (quality suffers) Good + Cheap → Not Fast (it'll take a long time)
region: United States / Canada

survey entry 334
type: SurveyResponse
date created: 2025-12-04T11:12:01.048
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Infrastructure / platform work, ML / AI, Fighting fires
storageenvironment: S3, Vespa
orchestration: Prefect
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: None / modeling is going well
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Not sure
educationtopic: Semantics / ontologies / knowledge graphs
region: Europe (EU / UK)

survey entry 335
type: SurveyResponse
date created: 2025-12-04T11:13:59.047
industry: Healthcare
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Control-M
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Governance / quality checks
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: Data quality
teamgrowth2026: Grow
educationtopic: Data modeling
region: United States / Canada

survey entry 336
type: SurveyResponse
date created: 2025-12-04T11:22:52.117
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 50–199
teamfocus: Ingestion / pipelines
storageenvironment: Mixed/hybrid
orchestration: Sequentum platform 100%
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Governance / quality checks
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: None / modeling is going well
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Shrink
educationtopic: Streaming / event-driven systems
industrywish: A holistic approach works best, you need methodology, standards, tooling, grit, cash...
region: United States / Canada

survey entry 337
type: SurveyResponse
date created: 2025-12-04T11:23:12.786
industry: Tech
job title: BI Developer
sic: this
naics: this
occupational category: this
spatial coverage: Middle East Africa
role: BI Developer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time
architecturetrend: Centralized warehouse
biggestbottleneck: Tool complexity
teamgrowth2026: Grow
educationtopic: Data modeling
region: Middle East / Africa

survey entry 338
type: SurveyResponse
date created: 2025-12-04T11:23:19.768
industry: Tech
job title: AI Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: AI Engineer
orgsize: 1,000–10,000
teamfocus: Infrastructure / platform work, ML / AI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Compute costs
teamgrowth2026: Grow
educationtopic: Streaming / event-driven systems
industrywish: The importance of data for AI to function well.
region: Asia–Pacific

survey entry 339
type: SurveyResponse
date created: 2025-12-04T11:25:04.047
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Infrastructure / platform work, Analytics / BI, ML / AI, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Data modeling, Architecture design, Governance / quality checks
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: AI/LLM integration
industrywish: LLMs, Agents, AI
region: Europe (EU / UK)

survey entry 340
type: SurveyResponse
date created: 2025-12-04T11:25:20.541
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Career growth / leadership
industrywish: I can do data analytics, but my job is to be the one who builds and maintains the platform for those who do data analytics.
region: United States / Canada

survey entry 341
type: SurveyResponse
date created: 2025-12-04T11:26:47.476
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 50–199
teamfocus: Data quality / reliability, Analytics / BI
storageenvironment: Mixed/hybrid
orchestration: No orchestration / ad-hoc
aiusagefrequency: Weekly
aihelpswith: Documentation / data discovery, Governance / quality checks
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Data modeling
industrywish: Data quality is critical building block.
region: United States / Canada

survey entry 342
type: SurveyResponse
date created: 2025-12-04T11:27:20.991
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Data quality / reliability, Analytics / BI, Fighting fires
storageenvironment: On-prem data warehouse
orchestration: Legacy bespoke solution
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Architecture patterns
region: Europe (EU / UK)

survey entry 343
type: SurveyResponse
date created: 2025-12-04T11:29:07.411
industry: Finance
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Middle East Africa
role: Data Architect
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Data modeling
industrywish: Proper Data Modeling is very important and can not be skipped
region: Middle East / Africa

survey entry 344
type: SurveyResponse
date created: 2025-12-04T11:29:45.926
industry: Finance
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: < 50 employees
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
industrywish: The importance of having a source of truth
region: United States / Canada

survey entry 345
type: SurveyResponse
date created: 2025-12-04T11:31:41.025
industry: Public sector / Education
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Architect
orgsize: 200–999
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Databricks tools
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Streaming / event-driven systems
industrywish: People don't understand the data ops side of the job. Most people expect that we deploy prototypes to prod by hand basically. Although we use prototyping strategically, the job of "productionalizing" (wishing we had a better word) is under-estimated.
region: United States / Canada

survey entry 346
type: SurveyResponse
date created: 2025-12-04T11:35:13.320
industry: Tech
job title: Software Engineer working on data
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Software Engineer working on data
orgsize: 200–999
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Data quality
teamgrowth2026: Grow
educationtopic: Streaming / event-driven systems
region: United States / Canada

survey entry 347
type: SurveyResponse
date created: 2025-12-04T11:36:00.989
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
region: Europe (EU / UK)

survey entry 348
type: SurveyResponse
date created: 2025-12-04T11:36:42.217
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Not sure
educationtopic: AI/LLM integration
region: Asia–Pacific

survey entry 349
type: SurveyResponse
date created: 2025-12-04T11:51:28.728
industry: Healthcare
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 50–199
teamfocus: Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: None / modeling is going well
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: AI/LLM integration
industrywish: That it's not all about coding.
region: United States / Canada

survey entry 350
type: SurveyResponse
date created: 2025-12-04T11:54:14.006
industry: Tech
job title: ML Engineer / MLOps
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: ML Engineer / MLOps
orgsize: 10,000+
teamfocus: ML / AI, Fighting fires
storageenvironment: Cloud PostgreSQL/MySQL
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
industrywish: There's a design/engineering aspect to it, not just rushed implementation.
region: United States / Canada

survey entry 351
type: SurveyResponse
date created: 2025-12-04T11:58:27.146
industry: Public sector / Education
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: 10,000+
teamfocus: Data modeling / transformation, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Databricks Jobs
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 352
type: SurveyResponse
date created: 2025-12-04T11:59:07.844
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 50–199
teamfocus: Data quality / reliability, Infrastructure / platform work
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Dagster
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership
architecturetrend: Lakehouse
biggestbottleneck: Lack of true data experience
teamgrowth2026: Grow
educationtopic: Data modeling
industrywish: Data is not static, data models evolve hence it must be a core principle not a feature
region: United States / Canada

survey entry 353
type: SurveyResponse
date created: 2025-12-04T11:59:34.859
industry: Retail/e-commerce
job title: Solution Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Solution Architect
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data quality / reliability, Infrastructure / platform work, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Architecture design
aiadoption: AI embedded in most workflows
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”, AI tools produce inconsistent schemas
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy Platforms
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
industrywish: Offshoring makes things more difficult
region: Europe (EU / UK)

survey entry 354
type: SurveyResponse
date created: 2025-12-04T12:00:05.838
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Dbrx Workflows
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Data modeling
region: United States / Canada

survey entry 355
type: SurveyResponse
date created: 2025-12-04T12:11:22.926
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: Data Engineer
orgsize: 200–999
teamfocus: Data modeling / transformation, Data quality / reliability, Analytics / BI
storageenvironment: Mixed/hybrid
orchestration: SQS, Step Functions, EMR
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Architecture design
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Shrink
educationtopic: Career growth / leadership
industrywish: Every software engineer can be a data engineer, but not every data engineer is a software engineer, and that's perfectly fine.
region: Latin America

survey entry 356
type: SurveyResponse
date created: 2025-12-04T12:14:01.284
industry: Manufacturing / Industrial
job title: MLOps, ML Engineer, Platform Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: MLOps, ML Engineer, Platform Engineer
orgsize: 1,000–10,000
teamfocus: Infrastructure / platform work, ML / AI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: DataBricks Pipeline
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: No meaningful adoption yet
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Reliability engineering
industrywish: DataOps
region: Europe (EU / UK)

survey entry 357
type: SurveyResponse
date created: 2025-12-04T12:16:33.893
industry: Healthcare
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 50–199
teamfocus: Ingestion / pipelines, Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Documentation / data discovery, Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Event-driven modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Event-driven architecture
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Reliability engineering
industrywish: The relationship w architecture and the opportunity to think about ROI in terms of realizing business value w the most efficient design—> execution.
region: United States / Canada

survey entry 358
type: SurveyResponse
date created: 2025-12-04T12:16:39.695
industry: Healthcare
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Data Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines, Data quality / reliability
storageenvironment: Mixed/hybrid
orchestration: SSIS / ADF
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Not sure
educationtopic: Data modeling
region: Australia / New Zealand

survey entry 359
type: SurveyResponse
date created: 2025-12-04T12:18:48.581
industry: Finance
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: On-prem data warehouse
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Architecture design
aiadoption: Building internal AI platforms
modelingapproach: One Big Table
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: Streaming / event-driven systems
region: Europe (EU / UK)

survey entry 360
type: SurveyResponse
date created: 2025-12-04T12:19:32.647
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 50–199
teamfocus: Data modeling / transformation, ML / AI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Dagster
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Architecture patterns
industrywish: If the glue is cheap, the product will fall apart eventually
region: Europe (EU / UK)

survey entry 361
type: SurveyResponse
date created: 2025-12-04T12:21:16.673
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 362
type: SurveyResponse
date created: 2025-12-04T12:52:54.748
industry: Healthcare
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 200–999
teamfocus: Data quality / reliability, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: dbt cloud & Airflow (in that order)
aiusagefrequency: Weekly
aihelpswith: Documentation / data discovery
aiadoption: AI embedded in most workflows
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Architecture patterns
region: United States / Canada

survey entry 363
type: SurveyResponse
date created: 2025-12-04T12:56:56.518
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Documentation / data discovery
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 364
type: SurveyResponse
date created: 2025-12-04T12:57:01.350
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: OPC
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Architecture design
aiadoption: Experimenting
modelingapproach: Data Vault
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Streaming / event-driven systems
region: Europe (EU / UK)

survey entry 365
type: SurveyResponse
date created: 2025-12-04T13:06:27.556
industry: Public sector / Education
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data quality / reliability
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 366
type: SurveyResponse
date created: 2025-12-04T13:07:37.087
industry: Tech
job title: Data Scientist
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Scientist
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Mixed/hybrid
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: Event-driven modeling
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Event-driven architecture
biggestbottleneck: Data quality
teamgrowth2026: Not sure
educationtopic: Reliability engineering
industrywish: You won’t get any answers to your business questions if it’s not a first class citizen in your planning (and software engineers should ideally be more educated on this topic when building schemas and eventing / and whenever they change them)
region: United States / Canada

survey entry 367
type: SurveyResponse
date created: 2025-12-04T13:17:24.612
industry: Manufacturing / Industrial
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Dagster
aiusagefrequency: Daily
aihelpswith: Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Data Vault
modelingpainpoints: Hard to maintain over time
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: Europe (EU / UK)

survey entry 368
type: SurveyResponse
date created: 2025-12-04T13:21:02.281
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Analytics / BI, ML / AI
storageenvironment: Mixed/hybrid
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Data modeling, Governance / quality checks
aiadoption: Experimenting
modelingapproach: Data Vault
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling
architecturetrend: Centralized warehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 369
type: SurveyResponse
date created: 2025-12-04T13:44:37.958
industry: Public sector / Education
job title: Platform Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Platform Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data quality / reliability
storageenvironment: Mixed/hybrid
orchestration: Dagster
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: No meaningful adoption yet
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Not sure
educationtopic: Architecture patterns
region: Europe (EU / UK)

survey entry 370
type: SurveyResponse
date created: 2025-12-04T13:46:41.673
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation, Infrastructure / platform work
storageenvironment: Cloud PostgreSQL/MySQL
orchestration: Dagster
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Hard to maintain over time
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Shrink
educationtopic: AI/LLM integration
industrywish: it's f****g time consuming
region: Europe (EU / UK)

survey entry 371
type: SurveyResponse
date created: 2025-12-04T14:14:25.222
industry: Public sector / Education
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 10,000+
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Rarely
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: No meaningful adoption yet
modelingapproach: Data Vault
modelingpainpoints: None / modeling is going well
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Streaming / event-driven systems
region: Europe (EU / UK)

survey entry 372
type: SurveyResponse
date created: 2025-12-04T14:20:35.070
industry: Manufacturing / Industrial
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 50–199
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: On-prem data warehouse
orchestration: Mulesoft
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Tools don’t support good modeling
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
industrywish: It's as much a business function as a technical one. And it's such a competitive advantage.
region: United States / Canada

survey entry 373
type: SurveyResponse
date created: 2025-12-04T14:28:51.012
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Talend :(
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Data Vault
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Streaming / event-driven systems
region: Europe (EU / UK)

survey entry 374
type: SurveyResponse
date created: 2025-12-04T14:43:22.342
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Cloud PostgreSQL/MySQL
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Architecture design
aiadoption: Experimenting
modelingapproach: Event-driven modeling
modelingpainpoints: Lack of clear ownership, None / modeling is going well
architecturetrend: Event-driven architecture
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: Europe (EU / UK)

survey entry 375
type: SurveyResponse
date created: 2025-12-04T14:49:47.408
industry: Finance
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: Analytics Engineer
orgsize: 50–199
teamfocus: Data modeling / transformation, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: Latin America

survey entry 376
type: SurveyResponse
date created: 2025-12-04T15:05:01.736
industry: Public sector / Education
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 200–999
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: On-prem data warehouse
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
region: Europe (EU / UK)

survey entry 377
type: SurveyResponse
date created: 2025-12-04T15:08:55.485
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Analytics / BI
storageenvironment: Mixed/hybrid
orchestration: No orchestration / ad-hoc
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Shrink
educationtopic: AI/LLM integration
industrywish: Data modeling
region: Europe (EU / UK)

survey entry 378
type: SurveyResponse
date created: 2025-12-04T15:10:49.305
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Rarely
aihelpswith: Architecture design, I don't find AI helpful
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: United States / Canada

survey entry 379
type: SurveyResponse
date created: 2025-12-04T15:39:17.295
industry: Public sector / Education
job title: Analyst
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analyst
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation, Infrastructure / platform work, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Compute costs
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 380
type: SurveyResponse
date created: 2025-12-04T15:48:58.409
industry: Finance
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 10,000+
teamfocus: Data modeling / transformation, Data quality / reliability, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: I don't know
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: Europe (EU / UK)

survey entry 381
type: SurveyResponse
date created: 2025-12-04T16:00:06.948
industry: Public sector / Education
job title: Nonprofit Director trying to learn data engineering
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Nonprofit Director trying to learn data engineering
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability
storageenvironment: Local machines. Im a beginner
orchestration: Kestra.io (learning it now)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Architecture design, Governance / quality checks
aiadoption: Building internal AI platforms
modelingapproach: Beginner
modelingpainpoints: None / modeling is going well
architecturetrend: Data orchestration
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Reliability engineering
industrywish: Im not qualified enough yet to answer. But my current thoughts is not to overload the Data Engineer with tasks that are more for Data Science and Analytics or other Data roles.
region: United States / Canada

survey entry 382
type: SurveyResponse
date created: 2025-12-04T16:00:39.984
industry: Tech
job title: ML Engineer / MLOps
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: ML Engineer / MLOps
orgsize: < 50 employees
teamfocus: Data modeling / transformation, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: One Big Table
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”, AI tools produce inconsistent schemas
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Data modeling
industrywish: AI needs a good Data Engineering pipeline to work
region: Latin America

survey entry 383
type: SurveyResponse
date created: 2025-12-04T16:18:38.163
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling
architecturetrend: Lakehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Not sure
educationtopic: Data modeling
region: Asia–Pacific

survey entry 384
type: SurveyResponse
date created: 2025-12-04T16:57:32.989
industry: Retail/e-commerce
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: 50–199
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 385
type: SurveyResponse
date created: 2025-12-04T17:04:34.901
industry: Healthcare
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Never
aihelpswith: I don't find AI helpful
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Stay the same
educationtopic: Career growth / leadership
industrywish: Data can't be an afterthought, these things take time to do! We have a cauldron, not a magic wand!
region: United States / Canada

survey entry 386
type: SurveyResponse
date created: 2025-12-04T17:08:25.170
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation, Analytics / BI, ML / AI, Fighting fires
storageenvironment: Mixed/hybrid
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Data modeling, Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
industrywish: That it is hard to build good data platforms and pipelines and data engineers can't fix data that doesn't exist / isn't captured
region: Australia / New Zealand

survey entry 387
type: SurveyResponse
date created: 2025-12-04T18:17:42.179
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Data modeling
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Shrink
educationtopic: Streaming / event-driven systems
region: Latin America

survey entry 388
type: SurveyResponse
date created: 2025-12-04T18:33:24.015
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Data Vault + Kimball Marts
modelingpainpoints: Pressure to “move fast”, AI tools produce inconsistent schemas
architecturetrend: Event-driven architecture
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: Data modeling. To Software Engineers, ingestion and pipelines make sense. Data modeling and proper data integration to them is a black box. Even product people and some in the business do not know nor care to know how time consuming it can be to model data correctly for humans and machines.
region: United States / Canada

survey entry 389
type: SurveyResponse
date created: 2025-12-04T18:53:46.337
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 10,000+
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Home grown/ proprietary
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Mix of Inmon, Kimball, OBT, semantic models
modelingpainpoints: Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
industrywish: There is much more to Semantic layer than BI like dimensions, measures, metrics etc
region: United States / Canada

survey entry 390
type: SurveyResponse
date created: 2025-12-04T18:57:04.644
industry: Healthcare
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Dagster
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Data modeling, Architecture design, Governance / quality checks
aiadoption: Building internal AI platforms
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: None / modeling is going well
architecturetrend: Lakehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: I believe AI is underhyped
region: United States / Canada

survey entry 391
type: SurveyResponse
date created: 2025-12-04T19:12:24.402
industry: Healthcare
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 10,000+
teamfocus: Data quality / reliability, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Documentation / data discovery, Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Data modeling
industrywish: Code quality matters
region: United States / Canada

survey entry 392
type: SurveyResponse
date created: 2025-12-04T19:37:25.093
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Manager / Director / VP
orgsize: < 50 employees
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
industrywish: It's the thing that matters more than data science or analytics
region: Asia–Pacific

survey entry 393
type: SurveyResponse
date created: 2025-12-04T20:37:56.876
industry: Public sector / Education
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Middle East Africa
role: Manager / Director / VP
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Infrastructure / platform work, Analytics / BI, ML / AI
storageenvironment: Mixed/hybrid
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: None / modeling is going well
architecturetrend: Lakehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: Middle East / Africa

survey entry 394
type: SurveyResponse
date created: 2025-12-04T21:30:10.739
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Analytics Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Analytics / BI, ML / AI
storageenvironment: On-prem data warehouse
orchestration: Dagster
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: No meaningful adoption yet
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Reliability engineering
region: Asia–Pacific

survey entry 395
type: SurveyResponse
date created: 2025-12-04T22:18:27.630
industry: Healthcare
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Analytics Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Data modeling
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Shrink
educationtopic: Career growth / leadership
industrywish: The importance of data engineering teams in an organization and how to manage them.
region: Asia–Pacific

survey entry 396
type: SurveyResponse
date created: 2025-12-04T23:00:33.432
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Rarely
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Centralized warehouse
biggestbottleneck: Huge numbers of externals building stuff with little oversight and zero quality control
teamgrowth2026: Shrink
educationtopic: DevOps / CI-CD
region: Europe (EU / UK)

survey entry 397
type: SurveyResponse
date created: 2025-12-04T23:05:18.003
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation, Analytics / BI, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: AI/LLM integration
industrywish: The best tech in the world will always be let down by poor business engagement & poor requirements!
region: Europe (EU / UK)

survey entry 398
type: SurveyResponse
date created: 2025-12-04T23:41:49.026
industry: Public sector / Education
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines, Data quality / reliability
storageenvironment: On-prem data warehouse
orchestration: Prefect
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: AI/LLM integration
industrywish: How important it is
region: United States / Canada

survey entry 399
type: SurveyResponse
date created: 2025-12-04T23:54:37.378
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 10,000+
teamfocus: Data modeling / transformation, ML / AI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Data modeling
industrywish: Good practices learned from software engineering matter for industrialisation. DE are not just data wizards but developers.
region: Europe (EU / UK)

survey entry 400
type: SurveyResponse
date created: 2025-12-05T00:27:45.164
industry: Retail/e-commerce
job title: Software Engineer working on data
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Software Engineer working on data
orgsize: 50–199
teamfocus: Infrastructure / platform work, ML / AI
storageenvironment: Mixed/hybrid
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: None / modeling is going well
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Shrink
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 401
type: SurveyResponse
date created: 2025-12-05T01:54:31.540
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data quality / reliability, Analytics / BI, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Databricks Jobs
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Data quality
teamgrowth2026: Grow
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 402
type: SurveyResponse
date created: 2025-12-05T03:07:57.826
industry: Healthcare
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: built-in Coalesce jobs
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Architecture patterns
industrywish: it shoulnt'be isolated from business
region: Europe (EU / UK)

survey entry 403
type: SurveyResponse
date created: 2025-12-05T03:24:52.087
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Data Vault
modelingpainpoints: Lack of clear ownership
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 404
type: SurveyResponse
date created: 2025-12-05T04:37:45.749
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Manager / Director / VP
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: No meaningful adoption yet
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Not sure
educationtopic: AI/LLM integration
region: Asia–Pacific

survey entry 405
type: SurveyResponse
date created: 2025-12-05T05:17:20.567
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Databricks
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 406
type: SurveyResponse
date created: 2025-12-05T05:24:05.834
industry: Retail/e-commerce
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 200–999
teamfocus: Ingestion / pipelines, Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Data modeling
aiadoption: Using AI for tactical tasks
modelingapproach: Event-driven modeling
modelingpainpoints: None / modeling is going well
architecturetrend: Lakehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 407
type: SurveyResponse
date created: 2025-12-05T05:49:49.318
industry: Retail/e-commerce
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 50–199
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Dagster
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Data modeling
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 408
type: SurveyResponse
date created: 2025-12-05T06:26:00.071
industry: Tech
job title: product manager
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: product manager
orgsize: < 50 employees
teamfocus: Data modeling / transformation
storageenvironment: On-prem data warehouse
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery, Data modeling
aiadoption: Using AI for tactical tasks
modelingapproach: conceptual data modeling only
modelingpainpoints: Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Not sure
educationtopic: Data modeling
region: United States / Canada

survey entry 409
type: SurveyResponse
date created: 2025-12-05T07:25:56.250
industry: Tech
job title: AI Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: AI Engineer
orgsize: 200–999
teamfocus: Data modeling / transformation, Infrastructure / platform work, Analytics / BI, ML / AI, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: dbt
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Data modeling, Architecture design, Governance / quality checks
aiadoption: Building internal AI platforms
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership
architecturetrend: Centralized warehouse
biggestbottleneck: Wrestling with eng on schema drift
teamgrowth2026: Shrink
educationtopic: Streaming / event-driven systems
region: United States / Canada

survey entry 410
type: SurveyResponse
date created: 2025-12-05T07:44:21.221
industry: Retail/e-commerce
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 50–199
teamfocus: Analytics / BI, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airbyte
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling
architecturetrend: Event-driven architecture
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: It's complicated to build data systems since you in principle have to take the full history of the company's systems into consideration for everything you build, while the rest of organization's developers are mainly concerned about the present state.
region: Europe (EU / UK)

survey entry 411
type: SurveyResponse
date created: 2025-12-05T07:49:44.191
industry: Retail/e-commerce
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Using AI for tactical tasks
modelingapproach: Event-driven modeling
modelingpainpoints: None / modeling is going well
architecturetrend: Event-driven architecture
biggestbottleneck: Tool complexity
teamgrowth2026: Grow
educationtopic: Streaming / event-driven systems
region: Europe (EU / UK)

survey entry 412
type: SurveyResponse
date created: 2025-12-05T07:57:40.940
industry: Tech
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Architect
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Governance / quality checks
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Event-driven architecture
biggestbottleneck: Ingestion costs
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: United States / Canada

survey entry 413
type: SurveyResponse
date created: 2025-12-05T08:21:21.528
industry: Finance
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 200–999
teamfocus: Analytics / BI, ML / AI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: dbt
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 414
type: SurveyResponse
date created: 2025-12-05T09:04:22.471
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation, Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Databricks jobs or dbt
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: None / modeling is going well
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: /
region: Europe (EU / UK)

survey entry 415
type: SurveyResponse
date created: 2025-12-05T09:10:12.494
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Infrastructure / platform work
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Compute costs
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 416
type: SurveyResponse
date created: 2025-12-05T09:17:48.742
industry: Manufacturing / Industrial
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 10,000+
teamfocus: Data quality / reliability, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Rarely
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: Data quality
teamgrowth2026: Grow
educationtopic: Career growth / leadership
region: United States / Canada

survey entry 417
type: SurveyResponse
date created: 2025-12-05T10:24:48.848
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Infrastructure / platform work, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: One Big Table
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”, AI tools produce inconsistent schemas
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Reliability engineering
region: Asia–Pacific

survey entry 418
type: SurveyResponse
date created: 2025-12-05T10:34:42.156
industry: Healthcare
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Fighting fires
storageenvironment: Mixed/hybrid
orchestration: Dagster
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Canonical/semantic models
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Career growth / leadership
industrywish: Data engineering is as much a technical role as it is an understanding the business role. A good data engineer will find that their business acumen will take them as far or farther than their technical acumen can. The industry should understand and embrace this.
region: United States / Canada

survey entry 419
type: SurveyResponse
date created: 2025-12-05T10:37:39.204
industry: Public sector / Education
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: 200–999
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Career growth / leadership
region: United States / Canada

survey entry 420
type: SurveyResponse
date created: 2025-12-05T10:54:43.368
industry: Finance
job title: AI Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: AI Engineer
orgsize: < 50 employees
teamfocus: Data quality / reliability, ML / AI
storageenvironment: Mixed/hybrid
orchestration: We focus mostly on agents and hence use alternative orchestration approaches and tools
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Architecture design
aiadoption: Building internal AI platforms
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Event-driven architecture
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Agents
industrywish: AI/Agent enabled data engineering is where I see the greatest pain point and the least amount of tooling support. I really wish the industry had the following: (a) "Cursor for Data engineering" which is not just pipeline building which they do already, but linking engineering work to business knowledge, semantic context etc), (b) Understanding the business - better knowledge management, not the mechanics (well understood) but capturing tacit knowledge or knowledge embedded in non-database domains... they key is not just AI-enablement, but agent-enablement where I have an agent "sidekick" that brings business knoweldge immediately or captures it at the time of need.
region: United States / Canada

survey entry 421
type: SurveyResponse
date created: 2025-12-05T11:22:30.309
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Fighting fires
storageenvironment: On-prem data warehouse
orchestration: No orchestration / ad-hoc
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: No meaningful adoption yet
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Event-driven architecture
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: How important to properly plan data governance and quality before using it downstream
region: Europe (EU / UK)

survey entry 422
type: SurveyResponse
date created: 2025-12-05T12:24:35.382
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: IBM Workload Scheduler
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Building internal AI platforms
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: United States / Canada

survey entry 423
type: SurveyResponse
date created: 2025-12-05T13:38:59.406
industry: Finance
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 50–199
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Mixed/hybrid
orchestration: Orchestra
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Data modeling
region: United States / Canada

survey entry 424
type: SurveyResponse
date created: 2025-12-05T15:34:26.211
industry: Healthcare
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: built in house: https://docs.napkin.run/getting-started/
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Event-driven architecture
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: United States / Canada

survey entry 425
type: SurveyResponse
date created: 2025-12-05T16:05:22.252
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Streaming / event-driven systems
region: United States / Canada

survey entry 426
type: SurveyResponse
date created: 2025-12-05T16:49:50.564
industry: Healthcare
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Architect
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Analytics / BI, Fighting fires
storageenvironment: Cloud IaaS
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Data modeling
aiadoption: No meaningful adoption yet
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling, AI tools produce inconsistent schemas
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: United States / Canada

survey entry 427
type: SurveyResponse
date created: 2025-12-05T17:06:56.047
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Never
aihelpswith: I don't find AI helpful
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Data quality
teamgrowth2026: Shrink
educationtopic: Data modeling
industrywish: Acknowledging it exists would be a start
region: Australia / New Zealand

survey entry 428
type: SurveyResponse
date created: 2025-12-05T18:50:07.655
industry: Public sector / Education
job title: ML Engineer / MLOps
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: ML Engineer / MLOps
orgsize: 1,000–10,000
teamfocus: ML / AI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Databricks Jobs via Databricks Asset Bundles
aiusagefrequency: Multiple times per day
aihelpswith: Pipeline debugging, Documentation / data discovery, Architecture design
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Reliability engineering
industrywish: Often it's assumed that it's "enough" if lots of data is available - there is too little talk or thought about the usefulness of the data and if it's available in formats that ML or analysts can work with without friction. The work to actually usable and used data is underestimated heavily.
region: Europe (EU / UK)

survey entry 429
type: SurveyResponse
date created: 2025-12-05T22:18:42.812
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation, Infrastructure / platform work, ML / AI, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”, AI tools produce inconsistent schemas
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Shrink
educationtopic: Career growth / leadership
industrywish: Clean data is a hard earned battle. It works because someone cares that it works.
region: United States / Canada

survey entry 430
type: SurveyResponse
date created: 2025-12-05T23:16:13.726
industry: Public sector / Education
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 200–999
teamfocus: Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: dbt Cloud
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: Europe (EU / UK)

survey entry 431
type: SurveyResponse
date created: 2025-12-06T01:53:50.827
industry: Retail/e-commerce
job title: ML Engineer / MLOps
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: ML Engineer / MLOps
orgsize: 1,000–10,000
teamfocus: ML / AI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Architecture design
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Reliability engineering
region: Europe (EU / UK)

survey entry 432
type: SurveyResponse
date created: 2025-12-06T02:22:39.196
industry: Finance
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: < 50 employees
teamfocus: Data modeling / transformation, Fighting fires
storageenvironment: Cloud PostgreSQL/MySQL
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
region: Europe (EU / UK)

survey entry 433
type: SurveyResponse
date created: 2025-12-06T02:25:37.054
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Azure Data Factory
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Tool complexity
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: Europe (EU / UK)

survey entry 434
type: SurveyResponse
date created: 2025-12-06T03:08:01.878
industry: Public sector / Education
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 200–999
teamfocus: Infrastructure / platform work, Fighting fires
storageenvironment: On-prem data warehouse
orchestration: FME
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Experimenting
modelingapproach: Canonical/semantic models
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Not sure
educationtopic: AI/LLM integration
industrywish: It's potential business value outside of reporting/analytics
region: Europe (EU / UK)

survey entry 435
type: SurveyResponse
date created: 2025-12-06T04:24:13.795
industry: Finance
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 200–999
teamfocus: Ingestion / pipelines, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Weekly
aihelpswith: I don't find AI helpful
aiadoption: No meaningful adoption yet
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
industrywish: Getting to value and driving KPIs
region: Europe (EU / UK)

survey entry 436
type: SurveyResponse
date created: 2025-12-06T05:47:01.048
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Engineer
orgsize: 50–199
teamfocus: Data quality / reliability, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time
architecturetrend: Centralized warehouse
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: Asia–Pacific

survey entry 437
type: SurveyResponse
date created: 2025-12-06T10:04:24.186
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Data quality / reliability, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: That data engineering, done correctly, is every bit as rigorous as software engineering.
region: United States / Canada

survey entry 438
type: SurveyResponse
date created: 2025-12-06T10:17:05.656
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: Data Engineer
orgsize: 10,000+
teamfocus: Infrastructure / platform work, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Culture
teamgrowth2026: Shrink
educationtopic: Architecture patterns
region: Latin America

survey entry 439
type: SurveyResponse
date created: 2025-12-06T11:06:10.359
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Pipeline debugging
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: AI tools produce inconsistent schemas
architecturetrend: Centralized warehouse
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: Asia–Pacific

survey entry 440
type: SurveyResponse
date created: 2025-12-06T13:29:21.566
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Fighting fires
storageenvironment: On-prem data warehouse
orchestration: Timextender
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Streaming / event-driven systems
region: Europe (EU / UK)

survey entry 441
type: SurveyResponse
date created: 2025-12-06T13:56:42.152
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Fighting fires
storageenvironment: Mixed/hybrid
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: No meaningful adoption yet
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Event-driven architecture
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: Australia / New Zealand

survey entry 442
type: SurveyResponse
date created: 2025-12-06T19:27:41.178
industry: Retail/e-commerce
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Dagster
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: One Big Table
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Shrink
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 443
type: SurveyResponse
date created: 2025-12-07T02:24:08.993
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 50–199
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Dagster
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Canonical/semantic models
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 444
type: SurveyResponse
date created: 2025-12-07T02:29:30.803
industry: Finance
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Architect
orgsize: 200–999
teamfocus: Data modeling / transformation, Data quality / reliability, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: No meaningful adoption yet
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: Asia–Pacific

survey entry 445
type: SurveyResponse
date created: 2025-12-07T04:41:33.046
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Data quality / reliability, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Pipeline debugging, Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Data Vault
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling, AI tools produce inconsistent schemas
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Architecture patterns
region: Europe (EU / UK)

survey entry 446
type: SurveyResponse
date created: 2025-12-07T04:54:59.615
industry: Manufacturing / Industrial
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Data quality / reliability, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 447
type: SurveyResponse
date created: 2025-12-07T05:10:29.912
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 50–199
teamfocus: Ingestion / pipelines, Data modeling / transformation, ML / AI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Prefect
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership
architecturetrend: Event-driven architecture
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 448
type: SurveyResponse
date created: 2025-12-07T05:53:33.219
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: Europe (EU / UK)

survey entry 449
type: SurveyResponse
date created: 2025-12-07T06:07:44.803
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Weekly
aihelpswith: Documentation / data discovery, I don't find AI helpful
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Shrink
educationtopic: Semantics / ontologies / knowledge graphs
region: Europe (EU / UK)

survey entry 450
type: SurveyResponse
date created: 2025-12-07T07:09:54.259
industry: Public sector / Education
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: AI embedded in most workflows
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Not sure
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: That the whole point of data engineering is to serve the business
region: Europe (EU / UK)

survey entry 451
type: SurveyResponse
date created: 2025-12-07T07:38:36.939
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Mixed/hybrid
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Streaming / event-driven systems
industrywish: A few years ago in an airport I saw an ad for a consulting firm that said: "Data is the new oil". But if someone dumped 10,000 barrels of oil on my doorstep what exactly could I do with it? Not of much value unless I can find someone with an oil refinery. If data is the new oil then then the value isn't the amount of raw data or where it came from. Data teams need to find their refinery. The bad news is refineries are complex and expensive. The good news is that those who have a refinery create/control immense value.
region: United States / Canada

survey entry 452
type: SurveyResponse
date created: 2025-12-07T08:17:12.720
industry: Tech
job title: Product manager
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Product manager
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Infrastructure / platform work, Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery, Data modeling, Architecture design
aiadoption: Experimenting
modelingapproach: Event-driven modeling
modelingpainpoints: Lack of clear ownership
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: Asia–Pacific

survey entry 453
type: SurveyResponse
date created: 2025-12-07T09:44:32.550
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Infrastructure / platform work, Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Data modeling
industrywish: That it takes time to build a good foundation, and that is a necessary exercise to go through
region: United States / Canada

survey entry 454
type: SurveyResponse
date created: 2025-12-07T11:05:57.899
industry: Retail/e-commerce
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 200–999
teamfocus: Data quality / reliability, Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Governance / quality checks
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: Without DE, there is no driving value from data.
region: Europe (EU / UK)

survey entry 455
type: SurveyResponse
date created: 2025-12-07T12:33:23.704
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: cronjobs
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 456
type: SurveyResponse
date created: 2025-12-07T13:30:11.660
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: Latin America

survey entry 457
type: SurveyResponse
date created: 2025-12-07T16:25:33.937
industry: Public sector / Education
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Manager / Director / VP
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Analytics / BI, Fighting fires
storageenvironment: Mixed/hybrid
orchestration: ODI, SQL, DBT
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Data modeling
industrywish: Data Modelling
region: Australia / New Zealand

survey entry 458
type: SurveyResponse
date created: 2025-12-07T20:28:32.282
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Architecture patterns
industrywish: Testing data is much harder than software
region: United States / Canada

survey entry 459
type: SurveyResponse
date created: 2025-12-08T01:15:20.537
industry: Finance
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 1,000–10,000
teamfocus: Analytics / BI, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Reliability engineering
region: Europe (EU / UK)

survey entry 460
type: SurveyResponse
date created: 2025-12-08T04:10:34.844
industry: Finance
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 200–999
teamfocus: Ingestion / pipelines, Fighting fires
storageenvironment: On-prem data warehouse
orchestration: No orchestration / ad-hoc
aiusagefrequency: Weekly
aihelpswith: Pipeline debugging
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Architecture patterns
region: Europe (EU / UK)

survey entry 461
type: SurveyResponse
date created: 2025-12-08T05:05:47.724
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 462
type: SurveyResponse
date created: 2025-12-08T05:06:05.794
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Datafactory + SQL server
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Experimenting
modelingapproach: 3NF
modelingpainpoints: Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Stay the same
educationtopic: Streaming / event-driven systems
industrywish: It takes time to build reliable systems
region: Europe (EU / UK)

survey entry 463
type: SurveyResponse
date created: 2025-12-08T05:49:38.728
industry: Manufacturing / Industrial
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 10,000+
teamfocus: Data quality / reliability, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 464
type: SurveyResponse
date created: 2025-12-08T06:54:56.583
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 200–999
teamfocus: Data modeling / transformation, ML / AI
storageenvironment: Mixed/hybrid
orchestration: Multiple
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: we are a consultancy, depends on the clients
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 465
type: SurveyResponse
date created: 2025-12-08T07:56:24.408
industry: Tech
job title: Data scientist
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data scientist
orgsize: 200–999
teamfocus: Data quality / reliability, ML / AI
storageenvironment: Mixed/hybrid
orchestration: Dagster
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: We aren't
biggestbottleneck: Data quality
teamgrowth2026: Grow
educationtopic: Data modeling
industrywish: Data modelling and integration is a political and business problem
region: Europe (EU / UK)

survey entry 466
type: SurveyResponse
date created: 2025-12-08T10:06:16.333
industry: Healthcare
job title: BI Specialist
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: BI Specialist
orgsize: 10,000+
teamfocus: Data quality / reliability, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: SSIS
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Data modeling
aiadoption: No meaningful adoption yet
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership
architecturetrend: Centralized warehouse
biggestbottleneck: Data silos, Relying on older tools, Lack of a data quality framework
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: Data Engineering is not just about moving data from point A to B, it's about creating a reliable and scalable foundation that won't happen without incorporating software engineering best practices.
region: United States / Canada

survey entry 467
type: SurveyResponse
date created: 2025-12-08T10:41:24.300
industry: Public sector / Education
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 200–999
teamfocus: Data modeling / transformation, Data quality / reliability, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery
aiadoption: Experimenting
modelingapproach: One Big Table + Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Talent / hiring
teamgrowth2026: Stay the same
educationtopic: Career growth / leadership
region: United States / Canada

survey entry 468
type: SurveyResponse
date created: 2025-12-08T14:44:57.728
industry: Finance
job title: AI Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: AI Engineer
orgsize: 1,000–10,000
teamfocus: Infrastructure / platform work
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: No meaningful adoption yet
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling, AI tools produce inconsistent schemas
architecturetrend: Event-driven architecture
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Streaming / event-driven systems
region: United States / Canada

survey entry 469
type: SurveyResponse
date created: 2025-12-08T15:25:03.610
industry: Finance
job title: Data Scientist
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Scientist
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation, Infrastructure / platform work
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: AI embedded in most workflows
modelingapproach: Canonical/semantic models
modelingpainpoints: Tools don’t support good modeling
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: United States / Canada

survey entry 470
type: SurveyResponse
date created: 2025-12-08T18:03:20.447
industry: Tech
job title: Software Engineer working on data
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Software Engineer working on data
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Dagster
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Governance / quality checks
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership
architecturetrend: Lakehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Architecture patterns
region: United States / Canada

survey entry 471
type: SurveyResponse
date created: 2025-12-08T22:22:58.262
industry: Finance
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 50–199
teamfocus: Ingestion / pipelines, Data modeling / transformation, Infrastructure / platform work, Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Governance / quality checks
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: None / modeling is going well
architecturetrend: Lakehouse
biggestbottleneck: Compute costs
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 472
type: SurveyResponse
date created: 2025-12-08T23:16:13.064
industry: Retail/e-commerce
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Analytics Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines, Analytics / BI
storageenvironment: Mixed/hybrid
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: Asia–Pacific

survey entry 473
type: SurveyResponse
date created: 2025-12-09T00:33:16.849
industry: Finance
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data quality / reliability, Infrastructure / platform work, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Tool complexity
teamgrowth2026: Shrink
educationtopic: Reliability engineering
industrywish: Stop using lingo like 'poor data quality' instead detail on what is wrong
region: Australia / New Zealand

survey entry 474
type: SurveyResponse
date created: 2025-12-09T00:37:08.307
industry: Retail/e-commerce
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Architect
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: ADF
aiusagefrequency: Rarely
aihelpswith: I don't find AI helpful
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Shrink
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 475
type: SurveyResponse
date created: 2025-12-09T03:35:12.586
industry: Retail/e-commerce
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Infrastructure / platform work
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Custom
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Data modeling
industrywish: How much it differs from classical Software Engineering and Data Science/BI
region: Europe (EU / UK)

survey entry 476
type: SurveyResponse
date created: 2025-12-09T05:08:28.100
industry: Healthcare
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 50–199
teamfocus: Ingestion / pipelines, Analytics / BI, Fighting fires
storageenvironment: On-prem data warehouse
orchestration: Stored procedures
aiusagefrequency: Daily
aihelpswith: Documentation / data discovery, Architecture design
aiadoption: No meaningful adoption yet
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Data quality
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: How brittle data ingestion can be and ways to make it more resilient.
region: United States / Canada

survey entry 477
type: SurveyResponse
date created: 2025-12-09T07:11:45.147
industry: Retail/e-commerce
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Infrastructure / platform work, Analytics / BI, Fighting fires
storageenvironment: Mixed/hybrid
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Data modeling
aiadoption: No meaningful adoption yet
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 478
type: SurveyResponse
date created: 2025-12-09T09:52:06.875
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: < 50 employees
teamfocus: Analytics / BI, ML / AI
storageenvironment: Cloud PostgreSQL/MySQL
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
industrywish: That it's the basis of AI
region: United States / Canada

survey entry 479
type: SurveyResponse
date created: 2025-12-09T12:59:42.733
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 50–199
teamfocus: Data modeling / transformation, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Data Vault
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 480
type: SurveyResponse
date created: 2025-12-09T17:52:42.220
industry: Tech
job title: Principal DevRel Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Principal DevRel Engineer
orgsize: < 50 employees
teamfocus: Data modeling / transformation, Infrastructure / platform work
storageenvironment: On-prem data warehouse
orchestration: No orchestration / ad-hoc
aiusagefrequency: Weekly
aihelpswith: Documentation / data discovery, Data modeling
aiadoption: AI embedded in most workflows
modelingapproach: Metadata modeling is required for the entity resolution workflows
modelingpainpoints: None / modeling is going well
architecturetrend: Event-driven architecture
biggestbottleneck: Documentation - not having perm links in Zendesk
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: There are no simple solutions, especially in regulated environments.
region: United States / Canada

survey entry 481
type: SurveyResponse
date created: 2025-12-09T21:43:12.616
industry: Manufacturing / Industrial
job title: Data Analyst
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Analyst
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Synapse
aiusagefrequency: Daily
aihelpswith: Pipeline debugging, Documentation / data discovery
aiadoption: No meaningful adoption yet
modelingapproach: Mixed (depends on use case)
modelingpainpoints: None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Not sure
educationtopic: Streaming / event-driven systems
region: United States / Canada

survey entry 482
type: SurveyResponse
date created: 2025-12-09T23:49:21.507
industry: Retail/e-commerce
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Middle East Africa
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Data quality / reliability, ML / AI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: the importance of data modeling
region: Middle East / Africa

survey entry 483
type: SurveyResponse
date created: 2025-12-10T03:14:48.093
industry: Healthcare
job title: Data Business Analyst or Product Owner
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Business Analyst or Product Owner
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Analytics / BI
storageenvironment: On-prem data warehouse
orchestration: No orchestration / ad-hoc
aiusagefrequency: Rarely
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Data modeling
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Not sure
educationtopic: AI/LLM integration
region: Asia–Pacific

survey entry 484
type: SurveyResponse
date created: 2025-12-10T03:18:44.105
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: < 50 employees
teamfocus: Ingestion / pipelines
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Pipeline debugging, Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Data modeling
industrywish: I wish people understand that data engineering is not all about tools. It about understanding the core concepts and fundamentals.
region: Europe (EU / UK)

survey entry 485
type: SurveyResponse
date created: 2025-12-10T07:29:08.559
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation, Fighting fires
storageenvironment: MSFT Fabric
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Rarely
aihelpswith: Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Tools don’t support good modeling, Pressure to “move fast”, AI tools produce inconsistent schemas
architecturetrend: Lakehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Reliability engineering
region: Asia–Pacific

survey entry 486
type: SurveyResponse
date created: 2025-12-10T09:14:58.236
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: None / modeling is going well
architecturetrend: Lakehouse
biggestbottleneck: Data quality
teamgrowth2026: Shrink
educationtopic: Architecture patterns
region: Asia–Pacific

survey entry 487
type: SurveyResponse
date created: 2025-12-10T10:24:44.280
industry: Manufacturing / Industrial
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Databricks Workflows
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Architecture patterns
industrywish: Turnkey no/low code solutions are a pipe dream.
region: Europe (EU / UK)

survey entry 488
type: SurveyResponse
date created: 2025-12-10T12:05:20.324
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: Data Engineer
orgsize: 50–199
teamfocus: Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Weekly
aihelpswith: Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: too much work for the leader
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: Latin America

survey entry 489
type: SurveyResponse
date created: 2025-12-10T15:27:55.667
industry: Tech
job title: SRE
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: SRE
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Data quality
teamgrowth2026: Grow
educationtopic: Reliability engineering
region: United States / Canada

survey entry 490
type: SurveyResponse
date created: 2025-12-10T15:59:46.034
industry: Manufacturing / Industrial
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation, Infrastructure / platform work, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: ADF
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Data modeling
aiadoption: No meaningful adoption yet
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
industrywish: Data quality can't be fixed by one person, team or department no matter how hard you shout.
region: Europe (EU / UK)

survey entry 491
type: SurveyResponse
date created: 2025-12-11T05:19:09.599
industry: Retail/e-commerce
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: No meaningful adoption yet
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time, None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
industrywish: How challenging enterprise level prioritisation of data engineering is. How to combat the assumption that data work is ‘one and done’ rather than an ongoing capability
region: Australia / New Zealand

survey entry 492
type: SurveyResponse
date created: 2025-12-11T07:51:01.578
industry: Healthcare
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Data modeling
industrywish: It takes long term investment in developing talent and mentorship to get right
region: United States / Canada

survey entry 493
type: SurveyResponse
date created: 2025-12-11T11:04:04.627
industry: Finance
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 200–999
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: United States / Canada

survey entry 494
type: SurveyResponse
date created: 2025-12-11T18:33:45.102
industry: Tech
job title: Software Engineer working on data
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Software Engineer working on data
orgsize: 200–999
teamfocus: Data modeling / transformation
storageenvironment: OLTP
orchestration: No orchestration / ad-hoc
aiusagefrequency: Never
aihelpswith: I don't find AI helpful
aiadoption: Experimenting
modelingapproach: Canonical/semantic models
modelingpainpoints: Pressure to “move fast”
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: Europe (EU / UK)

survey entry 495
type: SurveyResponse
date created: 2025-12-11T20:53:55.645
industry: Finance
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Manager / Director / VP
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Governance / quality checks
aiadoption: Building internal AI platforms
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
region: Asia–Pacific

survey entry 496
type: SurveyResponse
date created: 2025-12-12T09:18:50.558
industry: Retail/e-commerce
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data quality / reliability, Infrastructure / platform work, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 497
type: SurveyResponse
date created: 2025-12-12T11:20:05.394
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Infrastructure / platform work, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Reliability engineering
region: Latin America

survey entry 498
type: SurveyResponse
date created: 2025-12-12T15:11:51.780
industry: Healthcare
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data quality / reliability
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Databricks
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 499
type: SurveyResponse
date created: 2025-12-13T18:49:37.741
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 50–199
teamfocus: Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Governance / quality checks
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Event-driven architecture
biggestbottleneck: Data quality
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: Europe (EU / UK)

survey entry 500
type: SurveyResponse
date created: 2025-12-14T05:45:02.778
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 50–199
teamfocus: Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Data Vault
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: The code writing is the easiest part of the process by far
region: United States / Canada

survey entry 501
type: SurveyResponse
date created: 2025-12-14T07:29:32.127
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: No meaningful adoption yet
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 502
type: SurveyResponse
date created: 2025-12-14T10:30:29.713
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 50–199
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
region: Europe (EU / UK)

survey entry 503
type: SurveyResponse
date created: 2025-12-14T16:21:38.317
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: < 50 employees
teamfocus: Infrastructure / platform work, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 504
type: SurveyResponse
date created: 2025-12-14T19:42:16.658
industry: Public sector / Education
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: On-prem data warehouse
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: All of the above
industrywish: how critical this role is.
region: United States / Canada

survey entry 505
type: SurveyResponse
date created: 2025-12-15T03:30:19.999
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: < 50 employees
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Dagster
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: That Data Modelling isnt just something that the data engineers do, it has to be collaborative and owned in part by the business
region: Europe (EU / UK)

survey entry 506
type: SurveyResponse
date created: 2025-12-15T08:40:22.068
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Weekly
aihelpswith: Data modeling, Architecture design
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Reliability engineering
industrywish: How enteties are connected for his/her business
region: Europe (EU / UK)

survey entry 507
type: SurveyResponse
date created: 2025-12-15T16:03:15.204
industry: Healthcare
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 200–999
teamfocus: Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Weekly
aihelpswith: Pipeline debugging, Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 508
type: SurveyResponse
date created: 2025-12-15T19:40:11.430
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Engineer
orgsize: 10,000+
teamfocus: Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: Asia–Pacific

survey entry 509
type: SurveyResponse
date created: 2025-12-16T01:57:18.945
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 50–199
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Fivetran
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Governance / quality checks
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 510
type: SurveyResponse
date created: 2025-12-16T04:20:44.060
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: < 50 employees
teamfocus: Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery, Data modeling
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 511
type: SurveyResponse
date created: 2025-12-16T06:19:38.564
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: < 50 employees
teamfocus: Data quality / reliability, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Pipeline debugging, Governance / quality checks
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Data modeling
industrywish: Building "fast" very often introduces more problems than it solves. Staging and testing build trust which drive adoption ...
region: Europe (EU / UK)

survey entry 512
type: SurveyResponse
date created: 2025-12-16T07:23:29.044
industry: Finance
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Dagster
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: The invisible progress of data engineering is critical to do any of the visible magic of analytics and AI. committing to the discipline will seem like overkill in the short term, but will be money well spent in the medium term.
region: United States / Canada

survey entry 513
type: SurveyResponse
date created: 2025-12-16T08:19:58.430
industry: Tech
job title: AI Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: AI Engineer
orgsize: 50–199
teamfocus: Data modeling / transformation, Infrastructure / platform work
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: What the platform e.g. Databricks or MS Fabric offers out of the box
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Org very "structured data" oriented, does not know a lot about vector databases, knowledge mining in docs, enrichment (e.g. transcribing images in context of doc) etc.
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: How a reasonable Data Mesh approach works - beyond theory and in the age of AI. Now, AI is a consumer of datasets and may query tables, datasets, ontologies, ... How to design those data products in Data Mesh, how do we prevent excessive queries etc.
region: Europe (EU / UK)

survey entry 514
type: SurveyResponse
date created: 2025-12-17T06:58:03.547
industry: Healthcare
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation, Infrastructure / platform work
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Not sure
educationtopic: Semantics / ontologies / knowledge graphs
region: Europe (EU / UK)

survey entry 515
type: SurveyResponse
date created: 2025-12-17T07:11:03.033
industry: Healthcare
job title: Software Engineer working on data
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Software Engineer working on data
orgsize: < 50 employees
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Dagster
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 516
type: SurveyResponse
date created: 2025-12-17T08:59:18.402
industry: Finance
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: Analytics Engineer
orgsize: 200–999
teamfocus: Data modeling / transformation, Infrastructure / platform work
storageenvironment: Cloud PostgreSQL/MySQL
orchestration: No orchestration / ad-hoc
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Governance / quality checks
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Data quality
teamgrowth2026: Grow
educationtopic: Data modeling
region: Latin America

survey entry 517
type: SurveyResponse
date created: 2025-12-17T18:10:29.934
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Streaming / event-driven systems
region: United States / Canada

survey entry 518
type: SurveyResponse
date created: 2025-12-17T21:15:21.427
industry: Manufacturing / Industrial
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Analytics Engineer
orgsize: 200–999
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Pipeline debugging
aiadoption: No meaningful adoption yet
modelingapproach: Canonical/semantic models
modelingpainpoints: Lack of clear ownership
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Streaming / event-driven systems
region: Asia–Pacific

survey entry 519
type: SurveyResponse
date created: 2025-12-18T17:17:12.874
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation, Fighting fires
storageenvironment: On-prem data warehouse
orchestration: Airflow
aiusagefrequency: Rarely
aihelpswith: I don't find AI helpful
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”, AI tools produce inconsistent schemas
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Architecture patterns
industrywish: It isn't given particularly understood well or executed effectively
region: Europe (EU / UK)

survey entry 520
type: SurveyResponse
date created: 2025-12-18T17:17:32.580
industry: Manufacturing / Industrial
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: Data Architect
orgsize: 10,000+
teamfocus: Fighting fires
storageenvironment: Mixed/hybrid
orchestration: No orchestration / ad-hoc
aiusagefrequency: Rarely
aihelpswith: I don't find AI helpful
aiadoption: AI embedded in most workflows
modelingapproach: One Big Table
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Reliability engineering
region: Latin America

survey entry 521
type: SurveyResponse
date created: 2025-12-18T17:19:18.793
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Data modeling, Governance / quality checks
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling
architecturetrend: Centralized warehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Not sure
educationtopic: Data modeling
region: Asia–Pacific

survey entry 522
type: SurveyResponse
date created: 2025-12-18T17:22:24.463
industry: Manufacturing / Industrial
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Architect
orgsize: 200–999
teamfocus: Data modeling / transformation, Data quality / reliability, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Never
aihelpswith: I don't find AI helpful
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Tools don’t support good modeling
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Shrink
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: That it's just the first step in value creation; the ante into the poker game if you will.
region: United States / Canada

survey entry 523
type: SurveyResponse
date created: 2025-12-18T17:22:27.544
industry: Finance
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Prefer not to say
role: Data Architect
orgsize: 200–999
teamfocus: Data quality / reliability, Analytics / BI, ML / AI, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Governance / quality checks
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: Prefer not to say

survey entry 524
type: SurveyResponse
date created: 2025-12-18T17:23:08.926
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: < 50 employees
teamfocus: Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Hard to maintain over time
architecturetrend: Centralized warehouse
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 525
type: SurveyResponse
date created: 2025-12-18T17:26:53.710
industry: Retail/e-commerce
job title: All of the above
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: All of the above
orgsize: < 50 employees
teamfocus: Infrastructure / platform work, Fighting fires
storageenvironment: On-prem data warehouse
orchestration: Dagster
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling
architecturetrend: Event-driven architecture
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Not sure
educationtopic: Streaming / event-driven systems
industrywish: It's not just the data but the context of the data
region: United States / Canada

survey entry 526
type: SurveyResponse
date created: 2025-12-18T17:27:21.495
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: < 50 employees
teamfocus: Data quality / reliability, Infrastructure / platform work
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Governance / quality checks
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Career growth / leadership
industrywish: Good data engineering is not only about tool knowledge, but also about modeling and understanding the business pain points.
region: Europe (EU / UK)

survey entry 527
type: SurveyResponse
date created: 2025-12-18T17:27:50.098
industry: Manufacturing / Industrial
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Rarely
aihelpswith: Documentation / data discovery
aiadoption: No meaningful adoption yet
modelingapproach: Canonical/semantic models
modelingpainpoints: Lack of clear ownership
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Data Product Ownership
teamgrowth2026: Shrink
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: It’s not a commodity skill. Pressure to outsource
region: United States / Canada

survey entry 528
type: SurveyResponse
date created: 2025-12-18T17:30:27.400
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Maestro
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Data modeling
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Data modeling
industrywish: The pain points that dictated why the specialization exists.
region: United States / Canada

survey entry 529
type: SurveyResponse
date created: 2025-12-18T17:31:52.276
industry: Healthcare
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Using AI for tactical tasks
modelingapproach: One Big Table
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Architecture patterns
region: United States / Canada

survey entry 530
type: SurveyResponse
date created: 2025-12-18T17:33:30.178
industry: Tech
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Data Architect
orgsize: 1,000–10,000
teamfocus: Analytics / BI, ML / AI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: ADF
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery, Architecture design
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Tools don’t support good modeling, Pressure to “move fast”, AI tools produce inconsistent schemas
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
region: Australia / New Zealand

survey entry 531
type: SurveyResponse
date created: 2025-12-18T17:33:36.629
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: < 50 employees
teamfocus: Infrastructure / platform work, ML / AI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: databricks workflow
aiusagefrequency: Multiple times per day
aihelpswith: Pipeline debugging
aiadoption: Using AI for tactical tasks
modelingapproach: One Big Table
modelingpainpoints: Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Data quality
teamgrowth2026: Grow
educationtopic: Architecture patterns
industrywish: You can't as cowboy with data as with software
region: United States / Canada

survey entry 532
type: SurveyResponse
date created: 2025-12-18T17:34:16.663
industry: Manufacturing / Industrial
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Architect
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation
storageenvironment: On-prem data warehouse
orchestration: No orchestration / ad-hoc
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Data modeling
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 533
type: SurveyResponse
date created: 2025-12-18T17:34:20.283
industry: Healthcare
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Dagster
aiusagefrequency: Rarely
aihelpswith: Architecture design
aiadoption: No meaningful adoption yet
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Shrink
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: It takes significantly longer when understanding the business is our problem and not our stakeholder’s problem
region: United States / Canada

survey entry 534
type: SurveyResponse
date created: 2025-12-18T17:36:39.815
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: None / modeling is going well
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
industrywish: It’s still important
region: United States / Canada

survey entry 535
type: SurveyResponse
date created: 2025-12-18T17:36:45.759
industry: Retail/e-commerce
job title: Technology Strategist
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Technology Strategist
orgsize: 10,000+
teamfocus: Data modeling / transformation, Data quality / reliability, Infrastructure / platform work, Analytics / BI, ML / AI
storageenvironment: Lakehouse DWH + S3 lake + other DB stores (grapgh/docment)
orchestration: Primary (Glue/Spark) w EMR (spark) for large distributed worklaods) + others
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Data modeling
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: Data is a team sport, it requires sponsorship and alignment across both business and technical users to be fully successful.
region: Asia–Pacific

survey entry 536
type: SurveyResponse
date created: 2025-12-18T17:38:43.978
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 200–999
teamfocus: Data modeling / transformation, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Not sure
educationtopic: AI/LLM integration
industrywish: The range of what a data engineer does. It's a lot more than just pipeline building with python. There's all the hats one wears from building dashboards on BI tools, implementing AI where possible, maintenance of data platform, cost awareness, etc.
region: United States / Canada

survey entry 537
type: SurveyResponse
date created: 2025-12-18T17:38:52.018
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data quality / reliability
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Dagster
aiusagefrequency: Daily
aihelpswith: Documentation / data discovery, Data modeling
aiadoption: Experimenting
modelingapproach: One Big Table
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Data quality
teamgrowth2026: Shrink
educationtopic: Streaming / event-driven systems
industrywish: That it is a real specialization that can go toe to toe with any traditional software engineering. It’s the same mindset but it gets a bad wrap cause people don’t think “it’s hard to do”.
region: United States / Canada

survey entry 538
type: SurveyResponse
date created: 2025-12-18T17:42:10.013
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: 50–199
teamfocus: Data quality / reliability, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Not sure
educationtopic: Data modeling
region: United States / Canada

survey entry 539
type: SurveyResponse
date created: 2025-12-18T17:51:45.933
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 200–999
teamfocus: Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Shrink
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 540
type: SurveyResponse
date created: 2025-12-18T17:51:57.213
industry: Healthcare
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Governance / quality checks
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: AI/LLM integration
industrywish: Data Engineering is not as simple as says
region: Asia–Pacific

survey entry 541
type: SurveyResponse
date created: 2025-12-18T17:54:42.519
industry: Tech
job title: Software Engineer working on data
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Software Engineer working on data
orgsize: 1,000–10,000
teamfocus: Data quality / reliability
storageenvironment: Cloud PostgreSQL/MySQL
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: Event-driven modeling
modelingpainpoints: AI tools produce inconsistent schemas
architecturetrend: Centralized warehouse
biggestbottleneck: Data quality
teamgrowth2026: Grow
educationtopic: Career growth / leadership
region: Europe (EU / UK)

survey entry 542
type: SurveyResponse
date created: 2025-12-18T17:55:42.709
industry: Tech
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Architect
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Mixed/hybrid
orchestration: Async pub/sub ecosystem (Kafka)
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery, Data modeling
aiadoption: Using AI for tactical tasks
modelingapproach: Event-driven modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Event-driven architecture
biggestbottleneck: Tool complexity
teamgrowth2026: Stay the same
educationtopic: Data modeling
industrywish: Shifting left is a game changer. Not everything has to be loaded into a data warehouse. Async pub/sub is a great way to persist, share and reuse data.
region: United States / Canada

survey entry 543
type: SurveyResponse
date created: 2025-12-18T17:57:53.507
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Infrastructure / platform work, Analytics / BI, ML / AI, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Kafka
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Data modeling, Architecture design, Governance / quality checks
aiadoption: Building internal AI platforms
modelingapproach: Event-driven modeling
modelingpainpoints: Lack of clear ownership, None / modeling is going well
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Streaming / event-driven systems
region: United States / Canada

survey entry 544
type: SurveyResponse
date created: 2025-12-18T17:58:54.574
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data quality / reliability, Infrastructure / platform work, Fighting fires
storageenvironment: On-prem data warehouse
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: Retroactively remediating data models is difficult and cannot be solved by engineering teams alone
region: United States / Canada

survey entry 545
type: SurveyResponse
date created: 2025-12-18T17:59:05.487
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: 200–999
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Documentation / data discovery, Data modeling
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Data modeling
region: United States / Canada

survey entry 546
type: SurveyResponse
date created: 2025-12-18T17:59:24.853
industry: Manufacturing / Industrial
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 50–199
teamfocus: Data modeling / transformation, Data quality / reliability, Analytics / BI, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Rarely
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Experimenting
modelingapproach: Canonical/semantic models
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”, AI tools produce inconsistent schemas
architecturetrend: Event-driven architecture
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: There are no shortcuts to success - AI is not a magic bullet.
region: Europe (EU / UK)

survey entry 547
type: SurveyResponse
date created: 2025-12-18T18:07:19.740
industry: Retail/e-commerce
job title: Product Manager
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Product Manager
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership
architecturetrend: Centralized warehouse migrating to Lakehouse after being acquired
biggestbottleneck: Have a combination of legacy/tech debt and difficulties with leadership direction
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
industrywish: Data engineering is a strategic partner in achieving product outcomes, not just the exhaust of the traditional product triad (design, engineering, and PM)
region: United States / Canada

survey entry 548
type: SurveyResponse
date created: 2025-12-18T18:14:18.561
industry: Tech
job title: Data Modeller
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Data Modeller
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Rarely
aihelpswith: I don't find AI helpful
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: Australia / New Zealand

survey entry 549
type: SurveyResponse
date created: 2025-12-18T18:20:59.303
industry: Healthcare
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: github actions
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Data quality
teamgrowth2026: Grow
educationtopic: Streaming / event-driven systems
industrywish: that we aren't going away - but we need to expand our capabilities and partnerships with the business, and adapt to the realities of AI-driven development.
region: United States / Canada

survey entry 550
type: SurveyResponse
date created: 2025-12-18T18:23:45.188
industry: Tech
job title: Software Engineer working on data
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Software Engineer working on data
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Infrastructure / platform work, Analytics / BI
storageenvironment: Mixed/hybrid
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: No meaningful adoption yet
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: United States / Canada

survey entry 551
type: SurveyResponse
date created: 2025-12-18T18:44:59.414
industry: Public sector / Education
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Databricks jobs
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Tools don’t support good modeling
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 552
type: SurveyResponse
date created: 2025-12-18T18:49:36.047
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: < 50 employees
teamfocus: Analytics / BI
storageenvironment: On-prem data warehouse
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: Europe (EU / UK)

survey entry 553
type: SurveyResponse
date created: 2025-12-18T18:53:44.001
industry: Healthcare
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Architect
orgsize: 200–999
teamfocus: Data modeling / transformation, Data quality / reliability, Analytics / BI
storageenvironment: Microsoft Azure IaaS (SQL Server)
orchestration: SSIS
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Data modeling, Architecture design, Governance / quality checks
aiadoption: No meaningful adoption yet
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: United States / Canada

survey entry 554
type: SurveyResponse
date created: 2025-12-18T18:57:47.965
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: In house built
aiusagefrequency: Rarely
aihelpswith: Pipeline debugging, Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Canonical/semantic models
modelingpainpoints: Lack of clear ownership
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Not sure
educationtopic: Data modeling
industrywish: Orchestration
region: Europe (EU / UK)

survey entry 555
type: SurveyResponse
date created: 2025-12-18T18:58:35.426
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: Latin America

survey entry 556
type: SurveyResponse
date created: 2025-12-18T19:10:50.603
industry: Tech
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Data Architect
orgsize: 50–199
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: Compute costs
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: Australia / New Zealand

survey entry 557
type: SurveyResponse
date created: 2025-12-18T19:17:52.648
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: On-prem data warehouse
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 558
type: SurveyResponse
date created: 2025-12-18T19:25:01.678
industry: Public sector / Education
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Architect
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Cloud PostgreSQL/MySQL
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Tools don’t support good modeling
architecturetrend: Event-driven architecture
biggestbottleneck: Talent / hiring
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 559
type: SurveyResponse
date created: 2025-12-18T19:41:49.635
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Using AI for tactical tasks
modelingapproach: Canonical/semantic models
modelingpainpoints: None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: Compute costs
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 560
type: SurveyResponse
date created: 2025-12-18T19:50:56.314
industry: Healthcare
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Infrastructure / platform work, ML / AI
storageenvironment: Mixed/hybrid
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Going from Kimball to mixed with Lakehouse implementation
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Personally very interested in the eval data lifecycle underpinning AI-powered apps. None of the observability apps seem to get this right yet.
industrywish: I lead AI/ML and I wish the ratio of data engineers to data scientist and ai engineers was higher. They’re the lifeblood of our team. Especially those search engineer hybrids.
region: United States / Canada

survey entry 561
type: SurveyResponse
date created: 2025-12-18T19:57:31.365
industry: Tech
job title: Platform Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Platform Engineer
orgsize: < 50 employees
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: clickhouse and postgres selfhosted in AWS
orchestration: Mage
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: AI embedded in most workflows
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 562
type: SurveyResponse
date created: 2025-12-18T20:20:11.108
industry: Healthcare
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Data modeling
industrywish: dynamic linking between architecture and code
region: United States / Canada

survey entry 563
type: SurveyResponse
date created: 2025-12-18T20:26:14.424
industry: Finance
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Dbt schedules
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Canonical/semantic models
modelingpainpoints: Hard to maintain over time
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Data modeling
industrywish: Its value without having to justify it
region: Asia–Pacific

survey entry 564
type: SurveyResponse
date created: 2025-12-18T20:34:57.366
industry: Tech
job title: Consultant working on migrations and data quality
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Consultant working on migrations and data quality
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data quality / reliability
storageenvironment: Mixed/hybrid
orchestration: No orchestration / ad-hoc
aiusagefrequency: Rarely
aihelpswith: Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Event-driven modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Event-driven architecture
biggestbottleneck: Insufficient funding and staffing
teamgrowth2026: Stay the same
educationtopic: Reliability engineering
industrywish: Determining the value of the data and its usage so ROI of Data Engineering can be estimated
region: United States / Canada

survey entry 565
type: SurveyResponse
date created: 2025-12-18T20:35:20.096
industry: Public sector / Education
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
region: Australia / New Zealand

survey entry 566
type: SurveyResponse
date created: 2025-12-18T20:39:57.928
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data quality / reliability, Fighting fires
storageenvironment: Mixed/hybrid
orchestration: autosys
aiusagefrequency: Rarely
aihelpswith: Data modeling
aiadoption: Experimenting
modelingapproach: Data Vault
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: Technical aspects are later. Requirements and Business analysis is critical. Current State of the data flow and data consumption is critical before solutioning and technical aspects.
region: United States / Canada

survey entry 567
type: SurveyResponse
date created: 2025-12-18T20:47:09.230
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 50–199
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: We basically have cron based jobs but transformed models are orchestrated with a data transformation framework running prod jobs with a cloud run job on a schedule
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery, Data modeling
aiadoption: AI embedded in most workflows
modelingapproach: kimball at the core, OBT for reporting when needed
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Architecture patterns
industrywish: Pursue simplicity than complexity in your system, modeling, or pipeline
region: United States / Canada

survey entry 568
type: SurveyResponse
date created: 2025-12-18T20:50:54.524
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: < 50 employees
teamfocus: Infrastructure / platform work, ML / AI
storageenvironment: Cloud PostgreSQL/MySQL
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: None / modeling is going well
architecturetrend: Lakehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: How much we need business context to be effective or we just become too risk averse which slows us down and leads to an unhealthy spiral!
region: United States / Canada

survey entry 569
type: SurveyResponse
date created: 2025-12-18T21:06:41.059
industry: Finance
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: 10,000+
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: One Big Table
modelingpainpoints: Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Data modeling
region: United States / Canada

survey entry 570
type: SurveyResponse
date created: 2025-12-18T21:08:52.713
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 200–999
teamfocus: Data quality / reliability, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Documentation / data discovery, Governance / quality checks
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: United States / Canada

survey entry 571
type: SurveyResponse
date created: 2025-12-18T21:09:24.998
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data quality / reliability
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: AWS Step function state machines
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Not sure
educationtopic: Architecture patterns
industrywish: Software engineering and data engineering are starting to merge. Software engineers need to understand data engineering tools and skills and vice versa. Leadership and hiring managers need to understand this.
region: United States / Canada

survey entry 572
type: SurveyResponse
date created: 2025-12-18T21:21:46.406
industry: Finance
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: < 50 employees
teamfocus: Data modeling / transformation, Data quality / reliability, Analytics / BI
storageenvironment: Cloud PostgreSQL/MySQL
orchestration: Prefect
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: No meaningful adoption yet
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Data modeling
industrywish: Data ownership is politics. Data modeling in theory is too ideal. Data engineers are solving problems the upstream created, not really technical problems, political problems in a technical way.
region: United States / Canada

survey entry 573
type: SurveyResponse
date created: 2025-12-18T21:25:20.690
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: < 50 employees
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Dagster
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Event-driven modeling
modelingpainpoints: Tools don’t support good modeling
architecturetrend: Centralized warehouse
biggestbottleneck: Compute costs
teamgrowth2026: Stay the same
educationtopic: Streaming / event-driven systems
region: United States / Canada

survey entry 574
type: SurveyResponse
date created: 2025-12-18T21:34:57.290
industry: Manufacturing / Industrial
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 575
type: SurveyResponse
date created: 2025-12-18T21:50:55.597
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: < 50 employees
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Internal CRON Scheduler
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery, Data modeling
aiadoption: No meaningful adoption yet
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Data modeling
region: United States / Canada

survey entry 576
type: SurveyResponse
date created: 2025-12-18T22:24:18.917
industry: Manufacturing / Industrial
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Architect
orgsize: 10,000+
teamfocus: Analytics / BI, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Documentation / data discovery, Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: Intentional data modeling patterns is the flywheel for data engineering, BI, and AI.
region: United States / Canada

survey entry 577
type: SurveyResponse
date created: 2025-12-18T22:26:55.619
industry: Retail/e-commerce
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Architect
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data modeling / transformation, Analytics / BI, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: No meaningful adoption yet
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Architecture patterns
industrywish: Industry should understand that data engineering isn't a one-time job, it a end-to-end practice to become data-driven then ai-driven.
region: United States / Canada

survey entry 578
type: SurveyResponse
date created: 2025-12-18T22:43:32.748
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Data Engineer
orgsize: < 50 employees
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: No meaningful adoption yet
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Not sure
educationtopic: Data modeling
region: Australia / New Zealand

survey entry 579
type: SurveyResponse
date created: 2025-12-18T22:45:39.108
industry: Finance
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: < 50 employees
teamfocus: Data modeling / transformation, Infrastructure / platform work
storageenvironment: On-prem data warehouse
orchestration: Dagster
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 580
type: SurveyResponse
date created: 2025-12-18T23:21:18.182
industry: Public sector / Education
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 200–999
teamfocus: Infrastructure / platform work
storageenvironment: Mixed/hybrid
orchestration: Cron
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Data modeling
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Streaming / event-driven systems
industrywish: That source system quality directly effects everything downstream
region: Europe (EU / UK)

survey entry 581
type: SurveyResponse
date created: 2025-12-18T23:40:04.454
industry: Manufacturing / Industrial
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 1,000–10,000
teamfocus: Data quality / reliability, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery
aiadoption: No meaningful adoption yet
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Not sure
educationtopic: Semantics / ontologies / knowledge graphs
region: Europe (EU / UK)

survey entry 582
type: SurveyResponse
date created: 2025-12-18T23:48:51.947
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Prefer not to say
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation, Infrastructure / platform work, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Not sure
educationtopic: Data modeling
region: Prefer not to say

survey entry 583
type: SurveyResponse
date created: 2025-12-19T00:03:46.840
industry: Public sector / Education
job title: Platform Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Platform Engineer
orgsize: 1,000–10,000
teamfocus: Infrastructure / platform work
storageenvironment: On-prem data warehouse
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Experimenting
modelingapproach: No data modeling as platform team
modelingpainpoints: None / modeling is going well
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
industrywish: It's more about maturity and ability to change than fancies tools.
region: Europe (EU / UK)

survey entry 584
type: SurveyResponse
date created: 2025-12-19T00:06:51.897
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Not sure
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 585
type: SurveyResponse
date created: 2025-12-19T00:12:31.181
industry: Finance
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Architect
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Analytics / BI, ML / AI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Control M
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: Asia–Pacific

survey entry 586
type: SurveyResponse
date created: 2025-12-19T00:17:34.111
industry: Manufacturing / Industrial
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Fighting fires
storageenvironment: Mixed/hybrid
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery, Governance / quality checks
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
region: Australia / New Zealand

survey entry 587
type: SurveyResponse
date created: 2025-12-19T00:21:48.556
industry: Manufacturing / Industrial
job title: ML Engineer / MLOps
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: ML Engineer / MLOps
orgsize: 10,000+
teamfocus: Data modeling / transformation, Data quality / reliability, Infrastructure / platform work, ML / AI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: Mainly Kimball
modelingpainpoints: Lack of clear ownership
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Tool complexity
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: Europe (EU / UK)

survey entry 588
type: SurveyResponse
date created: 2025-12-19T00:31:00.728
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Analytics / BI, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Data modeling
industrywish: Data engineering is more than pipeline.
region: Europe (EU / UK)

survey entry 589
type: SurveyResponse
date created: 2025-12-19T00:38:40.089
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Bespoke .Net & Databricks Workflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Compute costs
teamgrowth2026: Grow
educationtopic: Streaming / event-driven systems
region: Europe (EU / UK)

survey entry 590
type: SurveyResponse
date created: 2025-12-19T00:38:41.201
industry: Retail/e-commerce
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 200–999
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Get data analysts to contribute to data tables
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: Technology is not an end. Bring all together stakeholders around the table to grow the business is the priority.
region: Europe (EU / UK)

survey entry 591
type: SurveyResponse
date created: 2025-12-19T00:55:28.916
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Not sure
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 592
type: SurveyResponse
date created: 2025-12-19T00:59:12.744
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Fighting fires
storageenvironment: Cloud PostgreSQL/MySQL
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery, Governance / quality checks
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: Don’t ditch quality as it gives an impression of slowing things down - if it does then you need to find competent people (lack of quality is very often due to incompetence)
region: Europe (EU / UK)

survey entry 593
type: SurveyResponse
date created: 2025-12-19T00:59:17.849
industry: Manufacturing / Industrial
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Fighting fires
storageenvironment: Mixed/hybrid
orchestration: Azure Data Factory (ADF)
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Canonical/semantic models
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Tool complexity
teamgrowth2026: Stay the same
educationtopic: Streaming / event-driven systems
industrywish: What goes in to achieving data quality
region: Europe (EU / UK)

survey entry 594
type: SurveyResponse
date created: 2025-12-19T01:06:27.128
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Analytics / BI
storageenvironment: On-prem data warehouse
orchestration: Dagster, ODI, pre-airflow custom built
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery, Data modeling, Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Stay the same
educationtopic: Data modeling
industrywish: With data engineering, competitive advantage compounds through faster, safer adaptation and reliable decisions; without it, any advantage is temporary, fragile, and eventually self-sabotaged by bad data.
region: Europe (EU / UK)

survey entry 595
type: SurveyResponse
date created: 2025-12-19T01:13:33.706
industry: Healthcare
job title: Platform Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Platform Engineer
orgsize: < 50 employees
teamfocus: Infrastructure / platform work
storageenvironment: Cloud PostgreSQL/MySQL
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: None / modeling is going well
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Data modeling
industrywish: It’s fundamental
region: Europe (EU / UK)

survey entry 596
type: SurveyResponse
date created: 2025-12-19T01:16:18.940
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Dbt
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Canonical/semantic models
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Streaming / event-driven systems
region: Europe (EU / UK)

survey entry 597
type: SurveyResponse
date created: 2025-12-19T01:16:22.759
industry: Finance
job title: Platform Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Platform Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Databricks
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Lack of business data competence
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: Data engineering suffers due to bad business modelling and low business integration
region: Europe (EU / UK)

survey entry 598
type: SurveyResponse
date created: 2025-12-19T01:19:29.400
industry: Manufacturing / Industrial
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 10,000+
teamfocus: Data quality / reliability
storageenvironment: On-prem data warehouse
orchestration: No orchestration / ad-hoc
aiusagefrequency: Rarely
aihelpswith: Architecture design, Governance / quality checks
aiadoption: Experimenting
modelingapproach: Canonical/semantic models
modelingpainpoints: Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
industrywish: Breaking Siloes is the core issue and the core solution, for architecture and data quality.
region: Europe (EU / UK)

survey entry 599
type: SurveyResponse
date created: 2025-12-19T01:44:33.229
industry: Retail/e-commerce
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Analytics Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Python and SSIS
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Experimenting
modelingapproach: One Big Table
modelingpainpoints: None / modeling is going well
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
industrywish: DE part in the data and BI value chain
region: Australia / New Zealand

survey entry 600
type: SurveyResponse
date created: 2025-12-19T01:48:17.303
industry: Tech
job title: ML Engineer / MLOps
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: ML Engineer / MLOps
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data quality / reliability, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Architecture design
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: None / modeling is going well
architecturetrend: Event-driven architecture
biggestbottleneck: Talent / hiring
teamgrowth2026: Shrink
educationtopic: Career growth / leadership
region: Europe (EU / UK)

survey entry 601
type: SurveyResponse
date created: 2025-12-19T02:08:30.072
industry: Public sector / Education
job title: Data analyst
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data analyst
orgsize: 50–199
teamfocus: Ingestion / pipelines, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: No meaningful adoption yet
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Architecture patterns
region: Europe (EU / UK)

survey entry 602
type: SurveyResponse
date created: 2025-12-19T02:10:21.131
industry: Retail/e-commerce
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data quality / reliability
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Build in databricks jobs
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Tool complexity
teamgrowth2026: Grow
educationtopic: Architecture patterns
region: Europe (EU / UK)

survey entry 603
type: SurveyResponse
date created: 2025-12-19T02:17:30.676
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”, AI tools produce inconsistent schemas
architecturetrend: Lakehouse
biggestbottleneck: Data quality
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: Europe (EU / UK)

survey entry 604
type: SurveyResponse
date created: 2025-12-19T02:44:22.817
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: No meaningful adoption yet
modelingapproach: Event-driven modeling
modelingpainpoints: None / modeling is going well
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: Asia–Pacific

survey entry 605
type: SurveyResponse
date created: 2025-12-19T03:06:16.666
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: Data Engineer
orgsize: 50–199
teamfocus: Analytics / BI, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Data quality
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: Latin America

survey entry 606
type: SurveyResponse
date created: 2025-12-19T03:25:17.103
industry: Manufacturing / Industrial
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation, Analytics / BI
storageenvironment: On-prem data warehouse
orchestration: Airflow
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Tools don’t support good modeling
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: Europe (EU / UK)

survey entry 607
type: SurveyResponse
date created: 2025-12-19T03:49:56.644
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
industrywish: You need to have solid data foundations - the basics that have been established for decades now: data quality, governance, appropriate modelling, consistent pipeline development approaches, solid operational structures and engineering practices in places (security, CI/CD, monitoring, alerting, finops, etc) - BEFORE deciding that you want to throw the AI bus into anything and everything, and expecting the LLM genie to get all your sheeeet gleaming up from nowhere just with the flick of a magic AI wand.
region: Europe (EU / UK)

survey entry 608
type: SurveyResponse
date created: 2025-12-19T03:55:13.529
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Data Engineer
orgsize: 50–199
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Mixed/hybrid
orchestration: Various for different purposes. Domo internal, dbt orchestration, n8n, make
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: AI/LLM integration
industrywish: It’s funner than what it looks like or sounds like.
region: Australia / New Zealand

survey entry 609
type: SurveyResponse
date created: 2025-12-19T03:57:30.368
industry: Public sector / Education
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, ML / AI
storageenvironment: On-prem data warehouse
orchestration: Airflow
aiusagefrequency: Weekly
aihelpswith: Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership
architecturetrend: I would hope we would have a consistent architecture by now, but it pain me to say, there isn't. In my circle of influence I try to adopt Data Mesh/ federated ownership to tackle this beast en show a good practice.
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: How to educate the next generation of bachelors to become Responsible data professionals, anything that helps with that, because foundational knowledge for this group is hard to get a hold of.
industrywish: It is impossible to be compliant to EU regulation without a data engineering lifecycle. Any vendor that professes it can be solved with AI is a dreamer. Data processing = communication and understanding, otherwise it is just moving data. I could write an essay about it, and maybe in the future I will, but I think your manifesto on data modeling says enough for now.
region: Europe (EU / UK)

survey entry 610
type: SurveyResponse
date created: 2025-12-19T03:58:38.236
industry: Finance
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 200–999
teamfocus: Data modeling / transformation, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Rarely
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 611
type: SurveyResponse
date created: 2025-12-19T04:09:38.364
industry: Tech
job title: Head of Development, architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Head of Development, architect
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud PostgreSQL/MySQL
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Canonical/semantic models
modelingpainpoints: None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: AI/LLM integration
industrywish: The importance of modeling.
region: Europe (EU / UK)

survey entry 612
type: SurveyResponse
date created: 2025-12-19T04:11:12.195
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Rarely
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: No meaningful adoption yet
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Data modeling
industrywish: I wish people would slow down with AI it stresses me. I just got a handle of how to code and build pipelines now it seems everything is put to work by AI
region: Europe (EU / UK)

survey entry 613
type: SurveyResponse
date created: 2025-12-19T04:25:02.769
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Azure Data Factory
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Data modeling
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: Asia–Pacific

survey entry 614
type: SurveyResponse
date created: 2025-12-19T04:32:36.158
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: Data Engineer
orgsize: 200–999
teamfocus: Analytics / BI, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Rarely
aihelpswith: Documentation / data discovery, I don't find AI helpful
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Shrink
educationtopic: Reliability engineering
region: Latin America

survey entry 615
type: SurveyResponse
date created: 2025-12-19T05:23:35.634
industry: Healthcare
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 50–199
teamfocus: Data modeling / transformation, Data quality / reliability, Fighting fires
storageenvironment: Mixed/hybrid
orchestration: SSIS
aiusagefrequency: Weekly
aihelpswith: Documentation / data discovery, Architecture design
aiadoption: No meaningful adoption yet
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
industrywish: Data pipelines are brittle, but they can be hardened with the right processes, tools, and people. The last part being extremely important. We need people who know the patterns of what works and what fails and why. I think AI struggles with this and it's amazing when you have a data engineer who has worked with many different data pipelines and can assess a situation and provide solutions that work and that are documented and they make their work approachable. They are not trying to be a hero, they are making their work reliable and approachable.
region: United States / Canada

survey entry 616
type: SurveyResponse
date created: 2025-12-19T05:25:17.292
industry: Healthcare
job title: Standards developer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Standards developer
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Mixed/hybrid
orchestration: No orchestration / ad-hoc
aiusagefrequency: Rarely
aihelpswith: I don't find AI helpful
aiadoption: Experimenting
modelingapproach: Canonical/semantic models
modelingpainpoints: None / modeling is going well
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Talent / hiring
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: More data does not necessarily mean better results/quality/etc.
region: Europe (EU / UK)

survey entry 617
type: SurveyResponse
date created: 2025-12-19T05:31:09.468
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: home made etl tool
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: No meaningful adoption yet
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Data modeling
industrywish: We wear multiple hats, please be kind to us and pay us what we actually deserve.
region: United States / Canada

survey entry 618
type: SurveyResponse
date created: 2025-12-19T05:59:15.128
industry: Manufacturing / Industrial
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Mixed/hybrid
orchestration: No orchestration / ad-hoc
aiusagefrequency: Rarely
aihelpswith: Governance / quality checks, I don't find AI helpful
aiadoption: No meaningful adoption yet
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: The importance of a well defined use case.
region: United States / Canada

survey entry 619
type: SurveyResponse
date created: 2025-12-19T06:13:46.690
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Engineer
orgsize: 200–999
teamfocus: Data modeling / transformation, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Data modeling
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time
architecturetrend: Centralized warehouse
biggestbottleneck: Overengineering
teamgrowth2026: Shrink
educationtopic: Career growth / leadership
industrywish: Team dysfunction shapes pipelines more than technology
region: Asia–Pacific

survey entry 620
type: SurveyResponse
date created: 2025-12-19T06:51:13.391
industry: Retail/e-commerce
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 200–999
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Dagster
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Data modeling
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 621
type: SurveyResponse
date created: 2025-12-19T07:09:55.990
industry: Healthcare
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Analytics / BI
storageenvironment: Mixed/hybrid
orchestration: Dagster
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Data modeling
industrywish: To get high quality results from data/automation, somebody has to manage the quality of your data. If an enterprise program for data quality doesn't exist you'll end up with the wild wild West some teams will do it well others won't.
region: United States / Canada

survey entry 622
type: SurveyResponse
date created: 2025-12-19T07:16:29.221
industry: Public sector / Education
job title: ML-DL Professor
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: ML-DL Professor
orgsize: 200–999
teamfocus: ML / AI
storageenvironment: Mixed/hybrid
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Data modeling
aiadoption: AI embedded in most workflows
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: AI tools produce inconsistent schemas
architecturetrend: Centralized warehouse
biggestbottleneck: Data quality
teamgrowth2026: Grow
educationtopic: Data modeling
industrywish: --
region: Asia–Pacific

survey entry 623
type: SurveyResponse
date created: 2025-12-19T07:18:23.034
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation, Analytics / BI
storageenvironment: Mixed/hybrid
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Event-driven architecture
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 624
type: SurveyResponse
date created: 2025-12-19T07:23:36.821
industry: Manufacturing / Industrial
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 1,000–10,000
teamfocus: Infrastructure / platform work, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Not sure
educationtopic: Data modeling
industrywish: It should be part of strategic conversations
region: Europe (EU / UK)

survey entry 625
type: SurveyResponse
date created: 2025-12-19T07:32:25.997
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: I think they understand, but a key improvement to products would be standards and better integration. It's often painful to put the tools together that you need for your case when they don't talk to each other natively.
region: United States / Canada

survey entry 626
type: SurveyResponse
date created: 2025-12-19T07:45:16.540
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Dagster
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Data modeling, Architecture design, Governance / quality checks
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Data modeling
industrywish: Data Engineer doesn't mean Data Engineer+Data Architect+Infrastructure Engineer+Devops+Security& Governance Specialist
region: Europe (EU / UK)

survey entry 627
type: SurveyResponse
date created: 2025-12-19T08:36:32.741
industry: Healthcare
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 10,000+
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Rarely
aihelpswith: Documentation / data discovery, Architecture design
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Architecture patterns
region: United States / Canada

survey entry 628
type: SurveyResponse
date created: 2025-12-19T08:54:10.054
industry: Tech
job title: Data Analyst
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Analyst
orgsize: 50–199
teamfocus: Ingestion / pipelines, Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Event-driven modeling
modelingpainpoints: Pressure to “move fast”, AI tools produce inconsistent schemas
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Reliability engineering
region: United States / Canada

survey entry 629
type: SurveyResponse
date created: 2025-12-19T09:02:20.137
industry: Healthcare
job title: Data Platform Product Manager
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Platform Product Manager
orgsize: 200–999
teamfocus: Data quality / reliability, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Event-driven modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: AI/LLM integration
industrywish: Data is everyone’s responsibility and there’s a direct dependency from strategy and operations on service owners and engineering. Data isn’t just an afterthought and analytics isn’t a series of one of report requests
region: United States / Canada

survey entry 630
type: SurveyResponse
date created: 2025-12-19T09:28:17.962
industry: Manufacturing / Industrial
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Architect
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Snowflake Tasks
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Tools don’t support good modeling
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: heterogenous tech debt cannot be replaced by a single tool/software solution
region: United States / Canada

survey entry 631
type: SurveyResponse
date created: 2025-12-19T09:31:52.031
industry: Manufacturing / Industrial
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: United States / Canada

survey entry 632
type: SurveyResponse
date created: 2025-12-19T09:46:56.222
industry: Manufacturing / Industrial
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: < 50 employees
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Dataform
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Data modeling
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 633
type: SurveyResponse
date created: 2025-12-19T09:50:53.831
industry: Healthcare
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Not sure, Databricks version of pipeline automation- we may not yet have dependencies
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership
architecturetrend: Centralized warehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 634
type: SurveyResponse
date created: 2025-12-19T10:39:16.158
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery, Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Architecture patterns
region: Europe (EU / UK)

survey entry 635
type: SurveyResponse
date created: 2025-12-19T11:00:53.659
industry: Finance
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: 50–199
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Governance / quality checks
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: None / modeling is going well
architecturetrend: Event-driven architecture
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: Data modeling
industrywish: How it directly impacts the business
region: United States / Canada

survey entry 636
type: SurveyResponse
date created: 2025-12-19T11:10:26.625
industry: Finance
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Rarely
aihelpswith: I don't find AI helpful
aiadoption: Experimenting
modelingapproach: One Big Table
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Data modeling
industrywish: It’s not magic and that good things come to those who wait
region: United States / Canada

survey entry 637
type: SurveyResponse
date created: 2025-12-19T11:10:42.932
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Databricks Lakeflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: AI embedded in most workflows
modelingapproach: Event-driven modeling
modelingpainpoints: Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Not sure
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 638
type: SurveyResponse
date created: 2025-12-19T11:17:50.161
industry: Retail/e-commerce
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Tooling and Shift Left adoption
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: Software and data are not the same, similar but different challenges.
region: Europe (EU / UK)

survey entry 639
type: SurveyResponse
date created: 2025-12-19T11:59:19.826
industry: Tech
job title: ML Engineer / MLOps
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: ML Engineer / MLOps
orgsize: < 50 employees
teamfocus: Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Dagster
aiusagefrequency: Multiple times per day
aihelpswith: Pipeline debugging, Documentation / data discovery
aiadoption: No meaningful adoption yet
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Streaming / event-driven systems
region: Europe (EU / UK)

survey entry 640
type: SurveyResponse
date created: 2025-12-19T12:01:28.251
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Data Engineer
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data quality / reliability, Infrastructure / platform work, Fighting fires
storageenvironment: Mixed/hybrid
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: Australia / New Zealand

survey entry 641
type: SurveyResponse
date created: 2025-12-19T12:26:40.150
industry: Manufacturing / Industrial
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: Manager / Director / VP
orgsize: 10,000+
teamfocus: Data modeling / transformation, Infrastructure / platform work
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: ADF (lol)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Data modeling
industrywish: No amount of technology or process improvements will make up for a lack of aligned definitions of key entities and metrics.
region: Latin America

survey entry 642
type: SurveyResponse
date created: 2025-12-19T12:32:13.030
industry: Retail/e-commerce
job title: Data Analyst
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Analyst
orgsize: 1,000–10,000
teamfocus: Analytics / BI
storageenvironment: Mixed/hybrid
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Building internal AI platforms
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: Career growth / leadership
industrywish: It needs time!
region: Europe (EU / UK)

survey entry 643
type: SurveyResponse
date created: 2025-12-19T12:44:21.422
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Data quality / reliability, Analytics / BI
storageenvironment: Cloud PostgreSQL/MySQL
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Experimenting
modelingapproach: Event-driven modeling
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Event-driven architecture
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 644
type: SurveyResponse
date created: 2025-12-19T12:59:59.196
industry: Manufacturing / Industrial
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data quality / reliability, Infrastructure / platform work, Fighting fires
storageenvironment: Mixed/hybrid
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery, Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: I wish the industry/its leaders, truly understood how important data and data engineering are, not just repeating buzzwords(big data, data driven...), but taking real action and making real investments.
region: Europe (EU / UK)

survey entry 645
type: SurveyResponse
date created: 2025-12-19T14:07:26.523
industry: Retail/e-commerce
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Infrastructure / platform work, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: No meaningful adoption yet
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Tools don’t support good modeling
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Career growth / leadership
region: Europe (EU / UK)

survey entry 646
type: SurveyResponse
date created: 2025-12-19T14:36:31.965
industry: Manufacturing / Industrial
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: No meaningful adoption yet
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Reliability engineering
region: United States / Canada

survey entry 647
type: SurveyResponse
date created: 2025-12-19T16:21:55.027
industry: Finance
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 50–199
teamfocus: Data modeling / transformation, Infrastructure / platform work, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Event based
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Hard to maintain over time
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Reliability engineering
industrywish: It should not ditch decades of software engineering best practices
region: Europe (EU / UK)

survey entry 648
type: SurveyResponse
date created: 2025-12-19T20:02:31.333
industry: Finance
job title: Data Engineer & Mainframe Developer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Engineer & Mainframe Developer
orgsize: 10,000+
teamfocus: Analytics / BI, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Google Cloud Scheduler
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Not sure
educationtopic: AI/LLM integration
industrywish: Clear understanding of roles and boundaries
region: Asia–Pacific

survey entry 649
type: SurveyResponse
date created: 2025-12-19T20:25:59.118
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Data modeling, Architecture design, Governance / quality checks
aiadoption: Building internal AI platforms
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Streaming / event-driven systems
industrywish: Systems behind pipelines, warehouses and dashboards. Understand why a particular system works the way it does and tradeoffs
region: Asia–Pacific

survey entry 650
type: SurveyResponse
date created: 2025-12-19T21:10:19.597
industry: Tech
job title: Software Engineer working on data
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Software Engineer working on data
orgsize: 50–199
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Cloud PostgreSQL/MySQL
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Data modeling, Architecture design
aiadoption: AI embedded in most workflows
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Architecture patterns
region: United States / Canada

survey entry 651
type: SurveyResponse
date created: 2025-12-19T21:26:45.528
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Data Engineer
orgsize: < 50 employees
teamfocus: Ingestion / pipelines
storageenvironment: Mixed/hybrid
orchestration: Mage
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Building internal AI platforms
modelingapproach: Event-driven modeling
modelingpainpoints: Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Event-driven architecture
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: That it requires just as much time and rigor as software engineering
region: Australia / New Zealand

survey entry 652
type: SurveyResponse
date created: 2025-12-19T23:34:21.713
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data quality / reliability, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Event-driven architecture
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 653
type: SurveyResponse
date created: 2025-12-19T23:42:18.820
industry: Manufacturing / Industrial
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Analytics / BI, Fighting fires
storageenvironment: On-prem data warehouse
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
industrywish: building robust data pipelines needs time and planning. Therefore the business context needs to be explained. It is not just „open the csv file and transfer it to database.“ This is not how data helps the collegues.
region: Europe (EU / UK)

survey entry 654
type: SurveyResponse
date created: 2025-12-20T01:38:26.722
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: On-prem data warehouse
orchestration: Dagster
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Reliability engineering
region: Europe (EU / UK)

survey entry 655
type: SurveyResponse
date created: 2025-12-20T02:34:36.608
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 10,000+
teamfocus: Infrastructure / platform work, ML / AI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Databricks Jobs
aiusagefrequency: Rarely
aihelpswith: I don't find AI helpful
aiadoption: No meaningful adoption yet
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: Europe (EU / UK)

survey entry 656
type: SurveyResponse
date created: 2025-12-20T05:35:05.828
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: AI embedded in most workflows
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: Compute costs
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 657
type: SurveyResponse
date created: 2025-12-20T07:06:23.499
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 200–999
teamfocus: Data modeling / transformation, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Talent, Lack of leadership direction, technical debt
teamgrowth2026: Shrink
educationtopic: Data modeling
industrywish: It is a company wide team effort.
region: Europe (EU / UK)

survey entry 658
type: SurveyResponse
date created: 2025-12-20T07:52:29.247
industry: Manufacturing / Industrial
job title: Software Engineer working on data
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Software Engineer working on data
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data quality / reliability
storageenvironment: Mixed/hybrid
orchestration: Prefect
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Architecture patterns
region: United States / Canada

survey entry 659
type: SurveyResponse
date created: 2025-12-20T08:20:16.644
industry: Tech
job title: Knowledge Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Knowledge Engineer
orgsize: < 50 employees
teamfocus: Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Building internal AI platforms
modelingapproach: Canonical/semantic models
modelingpainpoints: Tools don’t support good modeling
architecturetrend: Lakehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: Europe (EU / UK)

survey entry 660
type: SurveyResponse
date created: 2025-12-20T09:58:34.713
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Analytics / BI, ML / AI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Dagster
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Data modeling, Architecture design, Governance / quality checks
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Not sure
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 661
type: SurveyResponse
date created: 2025-12-21T06:42:23.674
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: Data Engineer
orgsize: 200–999
teamfocus: Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Data modeling
aiadoption: No meaningful adoption yet
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: Latin America

survey entry 662
type: SurveyResponse
date created: 2025-12-21T07:10:57.723
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: 50–199
teamfocus: Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Governance / quality checks
aiadoption: AI embedded in most workflows
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Event-driven architecture
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: United States / Canada

survey entry 663
type: SurveyResponse
date created: 2025-12-21T12:24:37.317
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Rarely
aihelpswith: Writing Code (SQL, Python, etc), I don't find AI helpful
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: Most data-heavy companies are fundamentally knowledge companies. Outsourcing (internationally or to AI) production and interconnection of knowledge is counterproductive.
region: United States / Canada

survey entry 664
type: SurveyResponse
date created: 2025-12-21T20:16:18.043
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 200–999
teamfocus: Data quality / reliability, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Weekly
aihelpswith: Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: None / modeling is going well
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Data quality
teamgrowth2026: Grow
educationtopic: Data modeling
region: United States / Canada

survey entry 665
type: SurveyResponse
date created: 2025-12-21T20:16:42.155
industry: Retail/e-commerce
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 666
type: SurveyResponse
date created: 2025-12-22T03:00:06.504
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 50–199
teamfocus: Data modeling / transformation
storageenvironment: On-prem data warehouse
orchestration: Hand built
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Reliability engineering
industrywish: That to build a good data model, I need a lot of time from business users to ask them how their world works.
region: Europe (EU / UK)

survey entry 667
type: SurveyResponse
date created: 2025-12-22T07:08:16.093
industry: Healthcare
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, ML / AI, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Databricks Workflows
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Experimenting
modelingapproach: Canonical/semantic models
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 668
type: SurveyResponse
date created: 2025-12-22T07:11:30.862
industry: Tech
job title: Software Engineer working on data
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Software Engineer working on data
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: in-house
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Architecture design
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”, AI tools produce inconsistent schemas
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
industrywish: "data contracts" = use an API
region: United States / Canada

survey entry 669
type: SurveyResponse
date created: 2025-12-22T12:26:06.763
industry: Public sector / Education
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: < 50 employees
teamfocus: Infrastructure / platform work, Analytics / BI
storageenvironment: Depends on the client, we are a management consulting firm
orchestration: No orchestration / ad-hoc
aiusagefrequency: Rarely
aihelpswith: Writing Code (SQL, Python, etc), Data modeling
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: all of the above, depends on the client
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Streaming / event-driven systems
region: United States / Canada

survey entry 670
type: SurveyResponse
date created: 2025-12-22T12:27:39.766
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation, Analytics / BI
storageenvironment: On-prem data warehouse
orchestration: SQL server Agent
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Shrink
educationtopic: Architecture patterns
region: United States / Canada

survey entry 671
type: SurveyResponse
date created: 2025-12-22T15:46:43.539
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: AI embedded in most workflows
modelingapproach: Data Vault
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Streaming / event-driven systems
region: United States / Canada

survey entry 672
type: SurveyResponse
date created: 2025-12-22T17:38:12.112
industry: Healthcare
job title: Software Engineer working on data
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Software Engineer working on data
orgsize: 200–999
teamfocus: Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Prefect
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery, Governance / quality checks
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: None / modeling is going well
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Compute costs
teamgrowth2026: Shrink
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 673
type: SurveyResponse
date created: 2025-12-23T01:21:54.215
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: On-prem data warehouse
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: No meaningful adoption yet
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling
architecturetrend: Centralized warehouse
biggestbottleneck: Compute costs
teamgrowth2026: Shrink
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 674
type: SurveyResponse
date created: 2025-12-23T08:19:32.068
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Event-driven modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Event-driven architecture
biggestbottleneck: Data quality
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: Europe (EU / UK)

survey entry 675
type: SurveyResponse
date created: 2025-12-23T15:06:47.756
industry: Healthcare
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: < 50 employees
teamfocus: Data quality / reliability, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery, Governance / quality checks
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 676
type: SurveyResponse
date created: 2025-12-23T15:49:42.725
industry: Finance
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 50–199
teamfocus: Data modeling / transformation, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Astronomer
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: The majority of data issues start at the source, until there is accountability in the teams generating the data there will always be a need for highly skilled data engineers to create customized transformation pipelines.
region: United States / Canada

survey entry 677
type: SurveyResponse
date created: 2025-12-24T09:52:10.723
industry: Finance
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Architect
orgsize: < 50 employees
teamfocus: Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: Canonical/semantic models
modelingpainpoints: None / modeling is going well
architecturetrend: Event-driven architecture
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 678
type: SurveyResponse
date created: 2025-12-25T01:51:03.611
industry: Finance
job title: Data analist
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data analist
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 679
type: SurveyResponse
date created: 2025-12-25T03:40:24.052
industry: Finance
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 10,000+
teamfocus: Data quality / reliability
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: None / modeling is going well
architecturetrend: Lakehouse
biggestbottleneck: Global vs local initiatives, sheer size of teams, aligning best practIces etc etc. List is long…
teamgrowth2026: Grow
educationtopic: ALL of the above? I handle multiple teams, plus global initiatives, so hard to frame some of my answers here.
industrywish: Data in movement vs data at rest, that we have to handle that duality
region: Europe (EU / UK)

survey entry 680
type: SurveyResponse
date created: 2025-12-26T09:17:32.966
industry: Retail/e-commerce
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: GitHub Actions FTW
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Shrink
educationtopic: Reliability engineering
region: Europe (EU / UK)

survey entry 681
type: SurveyResponse
date created: 2025-12-26T10:12:17.305
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Data quality
teamgrowth2026: Grow
educationtopic: AI/LLM integration
industrywish: I wish the wider industry understood the importance of data quality and the challenges we face in ensuring accurate and reliable data for analysis. By the way, we've been using surveyswap.io to get respondents. You fill in surveys and get responses back. Perhaps it's also helpful to you. Anyway, good luck!
region: Europe (EU / UK)

survey entry 682
type: SurveyResponse
date created: 2025-12-27T08:20:29.525
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Data modeling
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: Europe (EU / UK)

survey entry 683
type: SurveyResponse
date created: 2025-12-27T09:36:08.656
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Data modeling, Architecture design, Governance / quality checks
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”, AI tools produce inconsistent schemas
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Career growth / leadership
region: Asia–Pacific

survey entry 684
type: SurveyResponse
date created: 2025-12-27T09:43:06.663
industry: Healthcare
job title: Platform Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Platform Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Governance / quality checks
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: Asia–Pacific

survey entry 685
type: SurveyResponse
date created: 2025-12-27T09:48:05.369
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Databricks Workflows
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Data modeling
region: Latin America

survey entry 686
type: SurveyResponse
date created: 2025-12-27T09:54:36.126
industry: Healthcare
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Infrastructure / platform work, ML / AI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Data modeling, Architecture design, Governance / quality checks
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Shrink
educationtopic: Career growth / leadership
industrywish: I wish the wider industry understand how good data engineering enhances AI/ML.
region: Europe (EU / UK)

survey entry 687
type: SurveyResponse
date created: 2025-12-27T10:07:16.577
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: 200–999
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”, None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Not sure
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 688
type: SurveyResponse
date created: 2025-12-27T10:11:59.885
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Middle East Africa
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
region: Middle East / Africa

survey entry 689
type: SurveyResponse
date created: 2025-12-27T10:48:55.124
industry: Tech
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: Fancy tools and AI won't solve your data fundamentals; they're enablers, not magic sticks.
region: Europe (EU / UK)

survey entry 690
type: SurveyResponse
date created: 2025-12-27T10:52:58.199
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: 10,000+
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery, Data modeling
aiadoption: Building internal AI platforms
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: A quick fix will always come back to haunt you
region: United States / Canada

survey entry 691
type: SurveyResponse
date created: 2025-12-27T13:26:17.542
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airbyte
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Data modeling
aiadoption: AI embedded in most workflows
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Career growth / leadership
industrywish: Data Engineering :)
region: Europe (EU / UK)

survey entry 692
type: SurveyResponse
date created: 2025-12-27T14:24:02.789
industry: Tech
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Architect
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Mixed/hybrid
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Pipeline debugging, Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Canonical/semantic models
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: United States / Canada

survey entry 693
type: SurveyResponse
date created: 2025-12-27T19:22:10.755
industry: Healthcare
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: < 50 employees
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Data security (regulated industry specific, AI-specific)
industrywish: The balance between enforcing best practices and delivering a fast ROI is incredibly difficult.
region: United States / Canada

survey entry 694
type: SurveyResponse
date created: 2025-12-28T02:43:40.524
industry: Healthcare
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 10,000+
teamfocus: Data modeling / transformation, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Rarely
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Experimenting
modelingapproach: Canonical/semantic models
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Architecture patterns
region: Europe (EU / UK)

survey entry 695
type: SurveyResponse
date created: 2025-12-28T07:20:59.708
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data quality / reliability
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Streaming / event-driven systems
region: Europe (EU / UK)

survey entry 696
type: SurveyResponse
date created: 2025-12-28T08:25:20.715
industry: Finance
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Analytics / BI, ML / AI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Data modeling, Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: One Big Table
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 697
type: SurveyResponse
date created: 2025-12-28T10:35:50.407
industry: Tech
job title: Software Engineer working on data
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Software Engineer working on data
orgsize: 10,000+
teamfocus: Infrastructure / platform work, Fighting fires
storageenvironment: Mixed/hybrid
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery
aiadoption: AI embedded in most workflows
modelingapproach: Event-driven modeling
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Not sure
educationtopic: Reliability engineering
region: United States / Canada

survey entry 698
type: SurveyResponse
date created: 2025-12-28T21:34:02.848
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: 10,000+
teamfocus: Data modeling / transformation, Data quality / reliability, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery, Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Canonical/semantic models
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 699
type: SurveyResponse
date created: 2025-12-29T03:32:12.212
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 10,000+
teamfocus: Data modeling / transformation, Data quality / reliability, Analytics / BI, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Shrink
educationtopic: Career growth / leadership
industrywish: Complexity, time consuming
region: Europe (EU / UK)

survey entry 700
type: SurveyResponse
date created: 2025-12-29T06:57:37.794
industry: Manufacturing / Industrial
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: < 50 employees
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Orchestra
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 701
type: SurveyResponse
date created: 2025-12-29T10:45:58.165
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Data modeling
region: Latin America

survey entry 702
type: SurveyResponse
date created: 2025-12-29T12:45:33.433
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: 200–999
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Pipeline debugging, Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Career growth / leadership
industrywish: Data teams need non-data stakeholders to be part of the data process. We can enable the business to use data effectively. We can't do it for them.
region: United States / Canada

survey entry 703
type: SurveyResponse
date created: 2025-12-30T20:43:32.629
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Infrastructure / platform work, Analytics / BI, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: No meaningful adoption yet
modelingapproach: Mixed (depends on use case)
modelingpainpoints: None / modeling is going well
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: Europe (EU / UK)

survey entry 704
type: SurveyResponse
date created: 2025-12-30T23:43:11.335
industry: Healthcare
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: 50–199
teamfocus: Infrastructure / platform work, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Tools don’t support good modeling
architecturetrend: Centralized warehouse
biggestbottleneck: Tool complexity
teamgrowth2026: Grow
educationtopic: Data modeling
region: United States / Canada

survey entry 705
type: SurveyResponse
date created: 2025-12-31T09:46:16.726
industry: Public sector / Education
job title: Data Scientist
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Scientist
orgsize: < 50 employees
teamfocus: Data quality / reliability, Analytics / BI, Fighting fires
storageenvironment: Salesforce
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Tool complexity
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 706
type: SurveyResponse
date created: 2025-12-31T20:42:00.882
industry: Tech
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Architect
orgsize: 10,000+
teamfocus: Infrastructure / platform work, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Rarely
aihelpswith: Writing Code (SQL, Python, etc), I don't find AI helpful
aiadoption: AI embedded in most workflows
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Big Enterprise Company ... Disjoint Orgs: Very little overlap between complementary but independent teams/tools - ETL/Orchestration related and BI/Analytics.
teamgrowth2026: Grow
educationtopic: Data Consumption usage/patterns incl. Data Modeling and Analytics
industrywish: Involve DEs in the end to end data lifecycle incl BI/Analytics. Avoid unnecessary pre-materialization if, in turn, it helps make Analytics more responsive in small data situations => Some minimal requirement changes/adjacent-to-existing analytical tasks can be enabled.
region: Asia–Pacific

survey entry 707
type: SurveyResponse
date created: 2026-01-02T03:48:43.431
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Databricks
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 708
type: SurveyResponse
date created: 2026-01-02T04:41:03.839
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 50–199
teamfocus: Data quality / reliability, ML / AI
storageenvironment: Mixed/hybrid
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership
architecturetrend: Event-driven architecture
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: Europe (EU / UK)

survey entry 709
type: SurveyResponse
date created: 2026-01-02T08:35:33.336
industry: Healthcare
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: < 50 employees
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Documentation / data discovery, Governance / quality checks
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: AI/LLM integration
industrywish: Costs
region: United States / Canada

survey entry 710
type: SurveyResponse
date created: 2026-01-02T08:35:48.972
industry: Manufacturing / Industrial
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Data quality / reliability, Analytics / BI, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Data modeling, Governance / quality checks
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: changing mindset from legacy excel reports
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 711
type: SurveyResponse
date created: 2026-01-02T08:37:20.039
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Dagster
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Tools don’t support good modeling
architecturetrend: Lakehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Career growth / leadership
industrywish: Data engineering is impossible in isolation, it takes regular collaboration with teams across the business to ensure that deliveries provide value
region: Europe (EU / UK)

survey entry 712
type: SurveyResponse
date created: 2026-01-02T08:37:37.037
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 200–999
teamfocus: Data quality / reliability, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Rarely
aihelpswith: I don't find AI helpful
aiadoption: No meaningful adoption yet
modelingapproach: One Big Table
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: Europe (EU / UK)

survey entry 713
type: SurveyResponse
date created: 2026-01-02T08:39:30.591
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Infrastructure / platform work, ML / AI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: AI embedded in most workflows
modelingapproach: Event-driven modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 714
type: SurveyResponse
date created: 2026-01-02T08:39:35.670
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Prefer not to say
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Data quality / reliability, Infrastructure / platform work, Fighting fires
storageenvironment: Ab Initio Software
orchestration: Control M
aiusagefrequency: Rarely
aihelpswith: Writing Code (SQL, Python, etc), I don't find AI helpful
aiadoption: No meaningful adoption yet
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Stay the same
educationtopic: Streaming / event-driven systems
industrywish: It is more about process and people than technology.
region: Prefer not to say

survey entry 715
type: SurveyResponse
date created: 2026-01-02T08:39:54.415
industry: Tech
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Architect
orgsize: 50–199
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Databricks, Zapier
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery
aiadoption: No meaningful adoption yet
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Not sure
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 716
type: SurveyResponse
date created: 2026-01-02T08:40:17.026
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 50–199
teamfocus: Data modeling / transformation, Analytics / BI, ML / AI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Data modeling
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 717
type: SurveyResponse
date created: 2026-01-02T08:41:40.562
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Mixed/hybrid
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Architecture patterns
region: United States / Canada

survey entry 718
type: SurveyResponse
date created: 2026-01-02T08:42:25.722
industry: Finance
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 200–999
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Pipeline debugging, Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Data Vault
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 719
type: SurveyResponse
date created: 2026-01-02T08:42:32.572
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Middle East Africa
role: Analytics Engineer
orgsize: < 50 employees
teamfocus: Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership
architecturetrend: Centralized warehouse
biggestbottleneck: Compute costs
teamgrowth2026: Grow
educationtopic: Architecture patterns
region: Middle East / Africa

survey entry 720
type: SurveyResponse
date created: 2026-01-02T08:42:35.176
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Data modeling, Architecture design
aiadoption: AI embedded in most workflows
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Data modeling
industrywish: I wish my company and its consultancy clients understood that to build a solid product you need foundations and that takes time and compromise from both sides. There is still lack of knowledge regarding DE and Data Modelling best practices in many cases and that ends up with lousy deliverables.
region: Europe (EU / UK)

survey entry 721
type: SurveyResponse
date created: 2026-01-02T08:43:28.815
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: OpenSearch
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: One Big Table
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Data modeling
industrywish: Task completion throughput doesn't imply low team performance
region: Europe (EU / UK)

survey entry 722
type: SurveyResponse
date created: 2026-01-02T08:43:39.763
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Pipeline debugging
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: Asia–Pacific

survey entry 723
type: SurveyResponse
date created: 2026-01-02T08:45:06.061
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Azure Data Factory
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Reliability engineering
industrywish: Reducing complexity increases reliability. All these data modelling steps increase the amount of points of failure. We have a data vault model + kimball model, but it is very error prone, costly and slow. I do not think it was worth the effort.
region: Europe (EU / UK)

survey entry 724
type: SurveyResponse
date created: 2026-01-02T08:46:00.705
industry: Public sector / Education
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling
architecturetrend: Lakehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Data modeling
region: United States / Canada

survey entry 725
type: SurveyResponse
date created: 2026-01-02T08:46:07.932
industry: Healthcare
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 200–999
teamfocus: Data modeling / transformation, Fighting fires
storageenvironment: On-prem data warehouse
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: No meaningful adoption yet
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Architecture patterns
region: United States / Canada

survey entry 726
type: SurveyResponse
date created: 2026-01-02T08:46:37.795
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Matillion
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Streaming / event-driven systems
region: United States / Canada

survey entry 727
type: SurveyResponse
date created: 2026-01-02T08:46:46.931
industry: Healthcare
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 200–999
teamfocus: Analytics / BI, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Data modeling
industrywish: Humans actually need to think about and understand the organisations data - AI isn't going to do that for us
region: Europe (EU / UK)

survey entry 728
type: SurveyResponse
date created: 2026-01-02T08:48:04.712
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership
architecturetrend: Lakehouse
biggestbottleneck: Data quality
teamgrowth2026: Not sure
educationtopic: Streaming / event-driven systems
region: Europe (EU / UK)

survey entry 729
type: SurveyResponse
date created: 2026-01-02T08:49:14.791
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, ML / AI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Databricks
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership
architecturetrend: Lakehouse
biggestbottleneck: Compute costs
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 730
type: SurveyResponse
date created: 2026-01-02T08:52:23.154
industry: Finance
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data quality / reliability, Infrastructure / platform work, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Databricks Jobs
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 731
type: SurveyResponse
date created: 2026-01-02T08:52:31.637
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Pipeline debugging, Documentation / data discovery
aiadoption: No meaningful adoption yet
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: That it ain‘t cheap
region: Europe (EU / UK)

survey entry 732
type: SurveyResponse
date created: 2026-01-02T08:52:48.124
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Build kafka pipelines
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: No meaningful adoption yet
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: None / modeling is going well
architecturetrend: Event-driven architecture
biggestbottleneck: Compute costs
teamgrowth2026: Grow
educationtopic: Streaming / event-driven systems
industrywish: It's extremely multifacted, and no one person or team can do EVERYTHING, so be comfortable with imperfection.
region: United States / Canada

survey entry 733
type: SurveyResponse
date created: 2026-01-02T08:53:36.650
industry: Tech
job title: Sales engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Sales engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Mixed/hybrid
orchestration: Fivetran
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Data modeling
industrywish: Data engineering must provide business value or it's a wasted effort
region: United States / Canada

survey entry 734
type: SurveyResponse
date created: 2026-01-02T08:54:16.561
industry: Public sector / Education
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: On-prem data warehouse
orchestration: Dagu
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Experimenting
modelingapproach: Datavault + obt for marts
modelingpainpoints: None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 735
type: SurveyResponse
date created: 2026-01-02T08:54:28.270
industry: Manufacturing / Industrial
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Architect
orgsize: 10,000+
teamfocus: Data modeling / transformation, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Palantir Foundry
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
industrywish: You can't buy an organizational data transformation- you have to own it which means swallowing humility and putting organic people in charge who actually know what good DataOps looks like
region: United States / Canada

survey entry 736
type: SurveyResponse
date created: 2026-01-02T08:57:23.508
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: AI embedded in most workflows
modelingapproach: Event-driven modeling
modelingpainpoints: Pressure to “move fast”, AI tools produce inconsistent schemas
architecturetrend: Event-driven architecture
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
industrywish: It's complex, has many interdependences and takes time
region: Europe (EU / UK)

survey entry 737
type: SurveyResponse
date created: 2026-01-02T08:58:36.017
industry: Public sector / Education
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Mixed/hybrid
orchestration: Dagster
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: No meaningful adoption yet
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Talent / hiring
teamgrowth2026: Stay the same
educationtopic: Reliability engineering
region: Europe (EU / UK)

survey entry 738
type: SurveyResponse
date created: 2026-01-02T08:58:43.296
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Prefer not to say
role: Data Engineer
orgsize: < 50 employees
teamfocus: Data quality / reliability, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: No meaningful adoption yet
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Data quality
teamgrowth2026: Shrink
educationtopic: Architecture patterns
region: Prefer not to say

survey entry 739
type: SurveyResponse
date created: 2026-01-02T08:59:09.156
industry: Retail/e-commerce
job title: ML Engineer / MLOps
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: ML Engineer / MLOps
orgsize: 1,000–10,000
teamfocus: ML / AI, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 740
type: SurveyResponse
date created: 2026-01-02T08:59:26.845
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: Manager / Director / VP
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Fighting fires
storageenvironment: Mixed/hybrid
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Compute costs
teamgrowth2026: Not sure
educationtopic: Architecture patterns
region: Latin America

survey entry 741
type: SurveyResponse
date created: 2026-01-02T08:59:29.650
industry: Manufacturing / Industrial
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Building internal AI platforms
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 742
type: SurveyResponse
date created: 2026-01-02T08:59:39.069
industry: Retail/e-commerce
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, ML / AI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: dbt
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: Compute costs
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 743
type: SurveyResponse
date created: 2026-01-02T08:59:39.345
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 10,000+
teamfocus: Data modeling / transformation, Analytics / BI, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Azure Data Factory
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: No meaningful adoption yet
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Tools don’t support good modeling
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Data modeling
industrywish: Be rigorous in software engineering best practices. Leaders do not recognize the importance of it.
region: United States / Canada

survey entry 744
type: SurveyResponse
date created: 2026-01-02T08:59:44.501
industry: Tech
job title: ML Engineer / MLOps
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: ML Engineer / MLOps
orgsize: 200–999
teamfocus: Data quality / reliability, ML / AI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Data modeling, Architecture design, Governance / quality checks
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 745
type: SurveyResponse
date created: 2026-01-02T09:00:13.318
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: ADF and Databricks Job
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Streaming / event-driven systems
region: Europe (EU / UK)

survey entry 746
type: SurveyResponse
date created: 2026-01-02T09:00:49.867
industry: Finance
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 10,000+
teamfocus: Data modeling / transformation, Infrastructure / platform work
storageenvironment: Mixed/hybrid
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: AI embedded in most workflows
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction + talent/hiring
teamgrowth2026: Stay the same
educationtopic: Streaming / event-driven systems
industrywish: more clear understanding of what DE is supposed to do & more requirements (and knowledge transfers) about business context, not just "here's our SQL database, you can start working now"
region: Europe (EU / UK)

survey entry 747
type: SurveyResponse
date created: 2026-01-02T09:01:03.883
industry: Public sector / Education
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 200–999
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: On-prem data warehouse
orchestration: Kestra
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: Understand the impact of domain ownership, as an example it is not given that repositories can be combined in one central lake and it is not a case where IAM, RBAC, RLS or whatever is a solution.
region: Europe (EU / UK)

survey entry 748
type: SurveyResponse
date created: 2026-01-02T09:02:05.775
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 200–999
teamfocus: Data modeling / transformation, Infrastructure / platform work
storageenvironment: SQL Server on Azure
orchestration: SSIS
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
industrywish: Proper data stack
region: Europe (EU / UK)

survey entry 749
type: SurveyResponse
date created: 2026-01-02T09:05:20.666
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Middle East Africa
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Analytics / BI
storageenvironment: Mixed/hybrid
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Not sure
educationtopic: AI/LLM integration
region: Middle East / Africa

survey entry 750
type: SurveyResponse
date created: 2026-01-02T09:05:33.780
industry: Manufacturing / Industrial
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: AutoSys
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse w/federated ownership; matrixed in BU citizen DE and DS members
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: AI/LLM integration
industrywish: How much money is wasted in companies by not having a strategy in place to follow good data modeling practices that helps create reusable curated data structures. Large organizations will spend 10’s of millions in labor and system costs each year (BU’s each doing their own thing) while also having so much data sprawl that senior leadership teams struggle being data driven at the top. There is also a component of corporate alignment missing as well where data modeling and system design practices tend to follow org structure and culture - this is directly tied to data sprawl. However organizations can fix data sprawl by having a data-modeling-first strategy with senior leadership support (C suite and senior VP’s). This will help break down silos even when org structure naturally supports them (such as orgs built based on acquisitions).
region: United States / Canada

survey entry 751
type: SurveyResponse
date created: 2026-01-02T09:06:02.118
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 50–199
teamfocus: Data modeling / transformation, ML / AI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Experimenting
modelingapproach: One Big Table
modelingpainpoints: Lack of clear ownership
architecturetrend: Centralized warehouse
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
industrywish: We have domain expertise in data pipelines, AI/ML is an issue where more training is needed.
region: United States / Canada

survey entry 752
type: SurveyResponse
date created: 2026-01-02T09:06:27.167
industry: Healthcare
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Fighting fires
storageenvironment: Mixed/hybrid
orchestration: Legacy
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Event-driven architecture
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Not sure
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 753
type: SurveyResponse
date created: 2026-01-02T09:09:06.002
industry: Tech
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Middle East Africa
role: Data Architect
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: Middle East / Africa

survey entry 754
type: SurveyResponse
date created: 2026-01-02T09:09:42.828
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 50–199
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Reliability engineering
region: Europe (EU / UK)

survey entry 755
type: SurveyResponse
date created: 2026-01-02T09:09:44.694
industry: Healthcare
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 10,000+
teamfocus: Data modeling / transformation, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Rarely
aihelpswith: I don't find AI helpful
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Reliability engineering
industrywish: It’s not about metrics and KPIs but about being able to answer questions and the ability to ask “just one more thing” without any additional engineering effort.
region: United States / Canada

survey entry 756
type: SurveyResponse
date created: 2026-01-02T09:10:21.230
industry: Manufacturing / Industrial
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Analytics / BI
storageenvironment: Mixed/hybrid
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Stay the same
educationtopic: Career growth / leadership
region: Europe (EU / UK)

survey entry 757
type: SurveyResponse
date created: 2026-01-02T09:11:32.276
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Prefer not to say
role: Data Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines, Analytics / BI, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Data quality
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: Prefer not to say

survey entry 758
type: SurveyResponse
date created: 2026-01-02T09:11:41.021
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 50–199
teamfocus: Ingestion / pipelines, Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Data modeling, Architecture design, Governance / quality checks
aiadoption: AI embedded in most workflows
modelingapproach: Canonical/semantic models
modelingpainpoints: Tools don’t support good modeling
architecturetrend: Lakehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 759
type: SurveyResponse
date created: 2026-01-02T09:11:44.426
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Reliability engineering
region: United States / Canada

survey entry 760
type: SurveyResponse
date created: 2026-01-02T09:13:00.418
industry: Healthcare
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: 200–999
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
industrywish: Data Engineering/Modeling is a distinct discipline within, and even slightly outside of, software engineering. Just because the application you built uses Postgres, etc. on the backend does not mean you necessarily have a firm grasp of how those objects should be shaped and used for non-application use cases. This gap is evident whenever I've had to partner with backend engineers on projects.
region: United States / Canada

survey entry 761
type: SurveyResponse
date created: 2026-01-02T09:13:11.273
industry: Tech
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 50–199
teamfocus: Data modeling / transformation, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Event-driven architecture
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Streaming / event-driven systems
region: Europe (EU / UK)

survey entry 762
type: SurveyResponse
date created: 2026-01-02T09:14:06.271
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Fighting fires
storageenvironment: On-prem data warehouse
orchestration: CRON triggers via Talend Admin Centre
aiusagefrequency: Rarely
aihelpswith: Writing Code (SQL, Python, etc), Data modeling
aiadoption: No meaningful adoption yet
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Streaming / event-driven systems
industrywish: Not all businesses are handling “big data”
region: Europe (EU / UK)

survey entry 763
type: SurveyResponse
date created: 2026-01-02T09:14:24.923
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery
aiadoption: No meaningful adoption yet
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Data quality
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 764
type: SurveyResponse
date created: 2026-01-02T09:15:28.639
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 50–199
teamfocus: Ingestion / pipelines, Data quality / reliability
storageenvironment: Mixed/hybrid
orchestration: sequentum
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: None / modeling is going well
architecturetrend: Data mesh / federated ownership
biggestbottleneck: noise/confusion in the market
teamgrowth2026: Shrink
educationtopic: AI/LLM integration
industrywish: Web data from ai first scraping platforms is inconsistent and cannot match the quality (yet) of traditional scraping platforms. Also, the standards and lawsuits around web data are threatening the open web.
region: United States / Canada

survey entry 765
type: SurveyResponse
date created: 2026-01-02T09:15:36.777
industry: Finance
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Matillion
aiusagefrequency: Rarely
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: No meaningful adoption yet
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: Europe (EU / UK)

survey entry 766
type: SurveyResponse
date created: 2026-01-02T09:16:55.643
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Engineer
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Fighting fires
storageenvironment: On-prem data warehouse
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Not sure
educationtopic: AI/LLM integration
industrywish: That it is the backbone of the AI initiatives
region: Asia–Pacific

survey entry 767
type: SurveyResponse
date created: 2026-01-02T09:17:00.217
industry: Tech
job title: Field CTO (Sales)
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Field CTO (Sales)
orgsize: 200–999
teamfocus: Data modeling / transformation, Infrastructure / platform work
storageenvironment: Mixed/hybrid
orchestration: dbt
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Open formats
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Database fundamentals
industrywish: Medallion “architecture” will not save you
region: United States / Canada

survey entry 768
type: SurveyResponse
date created: 2026-01-02T09:17:05.323
industry: Public sector / Education
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Dagster
aiusagefrequency: Multiple times per day
aihelpswith: Pipeline debugging
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
industrywish: Americans are paid way too much. Third worlders don't care about quality for cultural reasons. Hire in Europe for the single most highly skilled people at less than half the cost
region: Europe (EU / UK)

survey entry 769
type: SurveyResponse
date created: 2026-01-02T09:17:05.407
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 1,000–10,000
teamfocus: Data quality / reliability, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 770
type: SurveyResponse
date created: 2026-01-02T09:17:51.263
industry: Manufacturing / Industrial
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 10,000+
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery, Governance / quality checks
aiadoption: Using AI for tactical tasks
modelingapproach: Canonical/semantic models
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Environment management for BI/data science teams (DevOps, but for teams that aren’t necessarily “dev”)
industrywish: When you work in a massive organization, even “basic” data engineering such as moving data from a source system to a raw data lake is cumbersome. The fact that industry standards and solutions exist is not an indication that they are fully leveraged inside a massive org.
region: United States / Canada

survey entry 771
type: SurveyResponse
date created: 2026-01-02T09:17:55.373
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Databricks Jobs
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Career growth / leadership
region: Europe (EU / UK)

survey entry 772
type: SurveyResponse
date created: 2026-01-02T09:18:46.657
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Data quality / reliability, Analytics / BI, ML / AI, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: AI tools produce inconsistent schemas
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Reliability engineering
region: United States / Canada

survey entry 773
type: SurveyResponse
date created: 2026-01-02T09:19:46.622
industry: Tech
job title: ML Engineer / MLOps
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: ML Engineer / MLOps
orgsize: < 50 employees
teamfocus: Infrastructure / platform work, ML / AI
storageenvironment: On-prem data warehouse
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: AI embedded in most workflows
modelingapproach: Event-driven modeling
modelingpainpoints: Pressure to “move fast”, AI tools produce inconsistent schemas
architecturetrend: Event-driven architecture
biggestbottleneck: cloud providers suck
teamgrowth2026: Stay the same
educationtopic: Data modeling
industrywish: Semantics, ontologies, knowledge graphs
region: United States / Canada

survey entry 774
type: SurveyResponse
date created: 2026-01-02T09:19:54.234
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Data modeling, Architecture design, Governance / quality checks
aiadoption: AI embedded in most workflows
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: Asia–Pacific

survey entry 775
type: SurveyResponse
date created: 2026-01-02T09:21:16.183
industry: Manufacturing / Industrial
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data quality / reliability
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: AI/LLM integration
industrywish: It's still the same old song, garbage in, garbage out.
region: Europe (EU / UK)

survey entry 776
type: SurveyResponse
date created: 2026-01-02T09:21:30.080
industry: Tech
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Architect
orgsize: 200–999
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Data modeling
industrywish: Data Contracts
region: United States / Canada

survey entry 777
type: SurveyResponse
date created: 2026-01-02T09:21:38.423
industry: Finance
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, ML / AI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud scheduler, but will use dagster soon
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership
architecturetrend: Centralized warehouse
biggestbottleneck: I am in martech team. Bottleneck is waiting for data team to ingest data for us
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
industrywish: That we need developers and other people to care about data quality for data if we are going to have data as a competetive advantage.
region: Europe (EU / UK)

survey entry 778
type: SurveyResponse
date created: 2026-01-02T09:21:39.984
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Dead on arrival
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Not sure
educationtopic: Data modeling
industrywish: No one has fucking big data, your stupid app won't grow to Google levels of scale and if it will you will have enough money to rebuild the whole thing from scratch. Stop throwing spark on everything. Also, lakehouses are just dwh with extra steps, more potential mistakes and no actual benefit.
region: Europe (EU / UK)

survey entry 779
type: SurveyResponse
date created: 2026-01-02T09:21:41.187
industry: Healthcare
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
industrywish: That the core to having good models and Gen Ai use case is still having robust , scalable and quality base data.
region: Asia–Pacific

survey entry 780
type: SurveyResponse
date created: 2026-01-02T09:21:41.754
industry: Retail/e-commerce
job title: Platform Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Platform Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”, AI tools produce inconsistent schemas
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: Europe (EU / UK)

survey entry 781
type: SurveyResponse
date created: 2026-01-02T09:21:43.619
industry: Finance
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 10,000+
teamfocus: Data quality / reliability, Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Documentation / data discovery, Governance / quality checks
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership
architecturetrend: Lakehouse
biggestbottleneck: Data quality
teamgrowth2026: Grow
educationtopic: Career growth / leadership
region: Europe (EU / UK)

survey entry 782
type: SurveyResponse
date created: 2026-01-02T09:22:17.999
industry: Healthcare
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 10,000+
teamfocus: Analytics / BI, ML / AI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Building internal AI platforms
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: AI/LLM integration
industrywish: That good data takes time and strategy to do it well
region: Europe (EU / UK)

survey entry 783
type: SurveyResponse
date created: 2026-01-02T09:22:18.502
industry: Manufacturing / Industrial
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: Manager / Director / VP
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: Canonical/semantic models
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: Latin America

survey entry 784
type: SurveyResponse
date created: 2026-01-02T09:22:46.066
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: < 50 employees
teamfocus: Data modeling / transformation, Infrastructure / platform work
storageenvironment: Cloud PostgreSQL/MySQL
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery, Data modeling
aiadoption: Using AI for tactical tasks
modelingapproach: Canonical/semantic models
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
industrywish: Data modeling is more important than ever
region: United States / Canada

survey entry 785
type: SurveyResponse
date created: 2026-01-02T09:23:33.034
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines, Data modeling / transformation, Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: ADF
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: AI embedded in most workflows
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”, AI tools produce inconsistent schemas
architecturetrend: Lakehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Stay the same
educationtopic: Reliability engineering
region: United States / Canada

survey entry 786
type: SurveyResponse
date created: 2026-01-02T09:25:21.474
industry: Healthcare
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 50–199
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Infrastructure / platform work
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Pipeline debugging, Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership
architecturetrend: Event-driven architecture
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: Quality and simplicity are king.
region: United States / Canada

survey entry 787
type: SurveyResponse
date created: 2026-01-02T09:25:37.921
industry: Manufacturing / Industrial
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: No meaningful adoption yet
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
industrywish: Garbage in, garbage out.
region: United States / Canada

survey entry 788
type: SurveyResponse
date created: 2026-01-02T09:26:39.757
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Infrastructure / platform work, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Unknown
aiusagefrequency: Weekly
aihelpswith: Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
industrywish: That a tactical solution that uses a Kimball approach and a strategic solution that uses an Inmon approach will require a lot of time to reconcile. Lots of organisations fail to reconcile what should be global and local which ends with a messy glocal outcome which helps nobody.
region: Europe (EU / UK)

survey entry 789
type: SurveyResponse
date created: 2026-01-02T09:26:47.723
industry: Healthcare
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Career growth / leadership
region: Europe (EU / UK)

survey entry 790
type: SurveyResponse
date created: 2026-01-02T09:27:48.240
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 791
type: SurveyResponse
date created: 2026-01-02T09:29:23.341
industry: Retail/e-commerce
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: Analytics Engineer
orgsize: 200–999
teamfocus: Analytics / BI, Fighting fires
storageenvironment: On-prem data warehouse
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Data modeling, Architecture design
aiadoption: No meaningful adoption yet
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
region: Latin America

survey entry 792
type: SurveyResponse
date created: 2026-01-02T09:29:46.055
industry: Healthcare
job title: Job seeker
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Job seeker
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data modeling / transformation, Analytics / BI, ML / AI
storageenvironment: Mixed/hybrid
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Compute costs
teamgrowth2026: Grow
educationtopic: Career growth / leadership
industrywish: Getting hired is next to impossible because HR are using rudimentary data engineering to sort most people out
region: United States / Canada

survey entry 793
type: SurveyResponse
date created: 2026-01-02T09:30:47.862
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 10,000+
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: On-prem data warehouse
orchestration: Ssis sql Agents
aiusagefrequency: Rarely
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Data modeling
industrywish: That its hard
region: Europe (EU / UK)

survey entry 794
type: SurveyResponse
date created: 2026-01-02T09:31:13.193
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Prefect
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 795
type: SurveyResponse
date created: 2026-01-02T09:31:23.759
industry: Tech
job title: AI Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: AI Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation, ML / AI
storageenvironment: Mixed/hybrid
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Career growth / leadership
region: United States / Canada

survey entry 796
type: SurveyResponse
date created: 2026-01-02T09:33:10.275
industry: Manufacturing / Industrial
job title: Software Engineer working on data
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Software Engineer working on data
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Databricks Lakeflow Jobs
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: No meaningful adoption yet
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: United States / Canada

survey entry 797
type: SurveyResponse
date created: 2026-01-02T09:33:24.219
industry: Manufacturing / Industrial
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 50–199
teamfocus: Data quality / reliability, Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Documentation / data discovery, Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 798
type: SurveyResponse
date created: 2026-01-02T09:33:29.425
industry: Public sector / Education
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Manager / Director / VP
orgsize: < 50 employees
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud PostgreSQL/MySQL
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: Asia–Pacific

survey entry 799
type: SurveyResponse
date created: 2026-01-02T09:33:45.743
industry: Manufacturing / Industrial
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Analytics / BI, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Custom metadata driven, running as Azure Functions
aiusagefrequency: Rarely
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Building internal AI platforms
modelingapproach: Data Vault
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
industrywish: That self-service data engineering may misleadingly produce results faster, but the long term cost is prohibitive
region: Europe (EU / UK)

survey entry 800
type: SurveyResponse
date created: 2026-01-02T09:34:40.413
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Middle East Africa
role: Analytics Engineer
orgsize: 200–999
teamfocus: Data modeling / transformation, Data quality / reliability, Analytics / BI
storageenvironment: On-prem data warehouse
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: Middle East / Africa

survey entry 801
type: SurveyResponse
date created: 2026-01-02T09:37:53.177
industry: Public sector / Education
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Databricks jobs
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Building internal AI platforms
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: Europe (EU / UK)

survey entry 802
type: SurveyResponse
date created: 2026-01-02T09:37:54.399
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 10,000+
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Dagster
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Talent / hiring
teamgrowth2026: Stay the same
educationtopic: Reliability engineering
region: Europe (EU / UK)

survey entry 803
type: SurveyResponse
date created: 2026-01-02T09:38:00.242
industry: Public sector / Education
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: 10,000+
teamfocus: Data quality / reliability, Fighting fires
storageenvironment: Mixed/hybrid
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: No meaningful adoption yet
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Career growth / leadership
industrywish: finding real value
region: United States / Canada

survey entry 804
type: SurveyResponse
date created: 2026-01-02T09:38:18.616
industry: Tech
job title: Data Product Manager
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Product Manager
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Data quality / reliability, Infrastructure / platform work, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Weekly
aihelpswith: Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: Europe (EU / UK)

survey entry 805
type: SurveyResponse
date created: 2026-01-02T09:38:44.120
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Infrastructure / platform work
storageenvironment: On-prem data warehouse
orchestration: NiFi
aiusagefrequency: Rarely
aihelpswith: Pipeline debugging, Documentation / data discovery
aiadoption: No meaningful adoption yet
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
industrywish: Data Engineering is not about tool switching based upon wants but tool hopping based upon requirements and architecture designs is ok.
region: Asia–Pacific

survey entry 806
type: SurveyResponse
date created: 2026-01-02T09:38:59.217
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: DataKitchen
aiusagefrequency: Daily
aihelpswith: Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: None / modeling is going well
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: Reliability engineering
industrywish: its all about team and process
region: United States / Canada

survey entry 807
type: SurveyResponse
date created: 2026-01-02T09:39:04.268
industry: Manufacturing / Industrial
job title: Platform Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Platform Engineer
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Infrastructure / platform work
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Azure based, Databricks workflows, Data Factory
aiusagefrequency: Multiple times per day
aihelpswith: Pipeline debugging, Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Architecture patterns
region: Europe (EU / UK)

survey entry 808
type: SurveyResponse
date created: 2026-01-02T09:42:05.576
industry: Manufacturing / Industrial
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Data quality / reliability, Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Azure Data Factory, Databricks
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Building internal AI platforms
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Data modeling
industrywish: Importance of data modeling, and the fact that SQL is not dead.
region: United States / Canada

survey entry 809
type: SurveyResponse
date created: 2026-01-02T09:42:42.247
industry: Retail/e-commerce
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 200–999
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: In-house Orchestrator tool ( something similar to Airflow )
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: One Big Table
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 810
type: SurveyResponse
date created: 2026-01-02T09:42:48.974
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Data modeling
aiadoption: Building internal AI platforms
modelingapproach: Canonical/semantic models
modelingpainpoints: Tools don’t support good modeling
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: Two things: That data engineering isn't about the tools, it's about understanding the business problems you're solving. Said differently, because I always seem to need more words than the average bear. For effen sakes, stop chasing every new framework in the modern data stack. Tools come and go, but the core principles don't change. ....And just because you never learned them or forgot they exist, doesn't mean we don't know what they are. Look it up or ask someone to point you to those who do and just listen and learn.
region: United States / Canada

survey entry 811
type: SurveyResponse
date created: 2026-01-02T09:43:04.403
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: No meaningful adoption yet
modelingapproach: Swamp
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Reliability engineering
region: Europe (EU / UK)

survey entry 812
type: SurveyResponse
date created: 2026-01-02T09:44:35.163
industry: Tech
job title: Software Engineer working on data
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Software Engineer working on data
orgsize: 1,000–10,000
teamfocus: Fighting fires
storageenvironment: Cloud PostgreSQL/MySQL
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Shrink
educationtopic: Architecture patterns
region: Asia–Pacific

survey entry 813
type: SurveyResponse
date created: 2026-01-02T09:46:41.436
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 200–999
teamfocus: Data modeling / transformation, Data quality / reliability, Analytics / BI, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: dbt Cloud
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Reliability engineering
region: Europe (EU / UK)

survey entry 814
type: SurveyResponse
date created: 2026-01-02T09:46:44.401
industry: Tech
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data quality / reliability, Infrastructure / platform work
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Databricks Workflows
aiusagefrequency: Daily
aihelpswith: Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Career growth / leadership
region: Europe (EU / UK)

survey entry 815
type: SurveyResponse
date created: 2026-01-02T09:49:18.327
industry: Manufacturing / Industrial
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 10,000+
teamfocus: Analytics / BI, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Tools don’t support good modeling
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Siloed platform teams
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: United States / Canada

survey entry 816
type: SurveyResponse
date created: 2026-01-02T09:49:20.328
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation, Fighting fires
storageenvironment: On-prem data warehouse
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Experimenting
modelingapproach: Data Vault
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
industrywish: That just replicating the data model from the source system is a recipe for disaster
region: Europe (EU / UK)

survey entry 817
type: SurveyResponse
date created: 2026-01-02T09:50:58.485
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Data quality / reliability, Infrastructure / platform work
storageenvironment: Mixed/hybrid
orchestration: Azkaban
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
industrywish: That learning tool and hire based on tool experience is not a great way of hiring
region: United States / Canada

survey entry 818
type: SurveyResponse
date created: 2026-01-02T09:52:31.762
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”, AI tools produce inconsistent schemas
architecturetrend: Lakehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Not sure
educationtopic: Low Level Data Processing
industrywish: Data Engineering is not an abstract version of the Software Engineering. Using the best practices from Software Engineering is critical while writing ETL | ELTs
region: Europe (EU / UK)

survey entry 819
type: SurveyResponse
date created: 2026-01-02T09:53:48.921
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Infrastructure / platform work, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Architecture design, Governance / quality checks
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Reliability engineering
region: Europe (EU / UK)

survey entry 820
type: SurveyResponse
date created: 2026-01-02T09:54:13.062
industry: Retail/e-commerce
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, AI tools produce inconsistent schemas
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Career growth / leadership
region: Europe (EU / UK)

survey entry 821
type: SurveyResponse
date created: 2026-01-02T09:55:02.653
industry: Tech
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 200–999
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: DBT/Dbt cloud
aiusagefrequency: Daily
aihelpswith: Pipeline debugging, Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
industrywish: It takes times, and doesn't work in agile mode
region: Europe (EU / UK)

survey entry 822
type: SurveyResponse
date created: 2026-01-02T09:55:10.877
industry: Manufacturing / Industrial
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Middle East Africa
role: Manager / Director / VP
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data quality / reliability
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: AI embedded in most workflows
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Tools don’t support good modeling
architecturetrend: Lakehouse
biggestbottleneck: Data quality
teamgrowth2026: Grow
educationtopic: Data modeling
industrywish: That IT team is not a Data Team!
region: Middle East / Africa

survey entry 823
type: SurveyResponse
date created: 2026-01-02T10:01:52.928
industry: Retail/e-commerce
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 200–999
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: No meaningful adoption yet
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: Europe (EU / UK)

survey entry 824
type: SurveyResponse
date created: 2026-01-02T10:02:15.716
industry: Tech
job title: Data & Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data & Analytics Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Analytics / BI, ML / AI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: dbt cloud
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Data modeling, Architecture design, Governance / quality checks
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: AI/LLM integration
industrywish: Data Quality needs combined effort from the upstream data producers to the consumers . Everything is not a data team problem to solve in the dark.
region: Europe (EU / UK)

survey entry 825
type: SurveyResponse
date created: 2026-01-02T10:04:57.307
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Analytics / BI, ML / AI, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: Real blocker are never technical problems
region: Europe (EU / UK)

survey entry 826
type: SurveyResponse
date created: 2026-01-02T10:05:16.529
industry: Healthcare
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 50–199
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: United States / Canada

survey entry 827
type: SurveyResponse
date created: 2026-01-02T10:05:48.294
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 50–199
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery, Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Streaming / event-driven systems
region: Europe (EU / UK)

survey entry 828
type: SurveyResponse
date created: 2026-01-02T10:08:21.952
industry: Finance
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Dagster
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Data modeling
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 829
type: SurveyResponse
date created: 2026-01-02T10:11:36.482
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Engineer
orgsize: 200–999
teamfocus: Analytics / BI
storageenvironment: Cloud PostgreSQL/MySQL
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Data modeling, Architecture design, Governance / quality checks
aiadoption: No meaningful adoption yet
modelingapproach: One Big Table
modelingpainpoints: Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Career growth / leadership
industrywish: Data engineering is not report sharing teams often come up to data team and ask for reports just for their presentation or validate their approach. A small team often get stuck in a loop just writing queries rather than focusing on pipelines
region: Asia–Pacific

survey entry 830
type: SurveyResponse
date created: 2026-01-02T10:14:01.654
industry: Finance
job title: Software Engineer working on data
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Software Engineer working on data
orgsize: 50–199
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Infrastructure / platform work, Analytics / BI
storageenvironment: Cloud PostgreSQL/MySQL
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Data modeling, Architecture design, Governance / quality checks
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Architecture patterns
region: Europe (EU / UK)

survey entry 831
type: SurveyResponse
date created: 2026-01-02T10:18:12.421
industry: Public sector / Education
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Data quality / reliability, Fighting fires
storageenvironment: Mixed/hybrid
orchestration: No orchestration / ad-hoc
aiusagefrequency: Weekly
aihelpswith: Documentation / data discovery, Governance / quality checks
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Not sure
educationtopic: AI/LLM integration
industrywish: Backend analytics and operations need to be considered when making front end changes to systems
region: United States / Canada

survey entry 832
type: SurveyResponse
date created: 2026-01-02T10:19:43.398
industry: Retail/e-commerce
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 200–999
teamfocus: Data modeling / transformation, Data quality / reliability, Fighting fires
storageenvironment: Mixed/hybrid
orchestration: No orchestration / ad-hoc
aiusagefrequency: Rarely
aihelpswith: I don't find AI helpful
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Data modeling
industrywish: Bring business people on board and foster communication to build awareness of data
region: Europe (EU / UK)

survey entry 833
type: SurveyResponse
date created: 2026-01-02T10:20:56.132
industry: Finance
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 1,000–10,000
teamfocus: Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: Data modeling
industrywish: Data modelling is necessary but seems to have been forgotten
region: Europe (EU / UK)

survey entry 834
type: SurveyResponse
date created: 2026-01-02T10:23:11.377
industry: Public sector / Education
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 200–999
teamfocus: Data modeling / transformation, Data quality / reliability, Fighting fires
storageenvironment: Mixed/hybrid
orchestration: Metadata driven on fabric
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Stay the same
educationtopic: Data modeling
industrywish: Ingested data does not mean that it can be served
region: Europe (EU / UK)

survey entry 835
type: SurveyResponse
date created: 2026-01-02T10:23:51.934
industry: Finance
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: Data Architect
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation
storageenvironment: Mixed/hybrid
orchestration: No orchestration / ad-hoc
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Streaming / event-driven systems
region: Latin America

survey entry 836
type: SurveyResponse
date created: 2026-01-02T10:25:15.793
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Engineer
orgsize: < 50 employees
teamfocus: Data modeling / transformation, Infrastructure / platform work
storageenvironment: On-prem data warehouse
orchestration: Dagster
aiusagefrequency: Rarely
aihelpswith: I don't find AI helpful
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Data modeling
region: Asia–Pacific

survey entry 837
type: SurveyResponse
date created: 2026-01-02T10:25:39.705
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Engineer
orgsize: 50–199
teamfocus: Infrastructure / platform work, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Compute costs
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
industrywish: our work is holding the fott
region: Asia–Pacific

survey entry 838
type: SurveyResponse
date created: 2026-01-02T10:27:51.129
industry: Finance
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 200–999
teamfocus: Data modeling / transformation
storageenvironment: Denodo data virtualization
orchestration: Denodo scheduler
aiusagefrequency: Weekly
aihelpswith: Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Tools don’t support good modeling
architecturetrend: Centralized warehouse
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 839
type: SurveyResponse
date created: 2026-01-02T10:28:05.473
industry: Finance
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 50–199
teamfocus: Ingestion / pipelines, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: AI/LLM integration
industrywish: The realities of "Data gravity".
region: United States / Canada

survey entry 840
type: SurveyResponse
date created: 2026-01-02T10:29:49.478
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data quality / reliability, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: AWS EventBridge Scheduler, Cron
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Architecture design, Governance / quality checks
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Data modeling
industrywish: I wish the industry understood that Data Engineers need to be domain experts, not just ticket-takers. Often, requests come in as simple data movement tasks, but technical skills alone aren't enough to guarantee reliability. To build trust, engineers need domain training just as much as they need technical specs. Without understanding the business context, we can only check for generic errors (like null values or record count checks). When we are trained in the domain, we can write business-specific Data Quality tests and robust reconciliation logic, ensuring that data doesn't just 'load' successfully, but actually matches the reality across source and target systems. Investing in this domain knowledge transforms us from utility providers into strategic partners who ensure data integrity.
region: Asia–Pacific

survey entry 841
type: SurveyResponse
date created: 2026-01-02T10:30:10.353
industry: Healthcare
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Lakeflow connect / databricks jobs
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Architecture design
aiadoption: Building internal AI platforms
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Architecture patterns
industrywish: It isn’t going anywhere anytime soon.
region: Europe (EU / UK)

survey entry 842
type: SurveyResponse
date created: 2026-01-02T10:30:38.296
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Infrastructure / platform work, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Governance / quality checks
aiadoption: Building internal AI platforms
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Event-driven architecture
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 843
type: SurveyResponse
date created: 2026-01-02T10:31:25.483
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Middle East Africa
role: Data Engineer
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Data modeling
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Data modeling
industrywish: That data engineering is far from data analytics, and that it entails much more than visualizations and reporting
region: Middle East / Africa

survey entry 844
type: SurveyResponse
date created: 2026-01-02T10:31:32.458
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Infrastructure / platform work, Analytics / BI, ML / AI, Fighting fires
storageenvironment: On-prem data warehouse
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Governance / quality checks
aiadoption: AI embedded in most workflows
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: AI/LLM integration
industrywish: Tech is table stakes. Modeling and good practices is the job. For new people, don't try to learn everything at once. Be strong at a couple of things then add skills incrementally, diving deep into some, in order to differentiate.
region: Latin America

survey entry 845
type: SurveyResponse
date created: 2026-01-02T10:34:19.913
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Engineer
orgsize: 200–999
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Data modeling, Governance / quality checks
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: Asia–Pacific

survey entry 846
type: SurveyResponse
date created: 2026-01-02T10:37:42.356
industry: Public sector / Education
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 10,000+
teamfocus: Data quality / reliability, Infrastructure / platform work, Analytics / BI
storageenvironment: On-prem data warehouse
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Weekly
aihelpswith: Governance / quality checks
aiadoption: Using AI for tactical tasks
modelingapproach: Event-driven modeling
modelingpainpoints: Lack of clear ownership
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 847
type: SurveyResponse
date created: 2026-01-02T10:40:56.224
industry: Healthcare
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Data modeling, Architecture design
aiadoption: Experimenting
modelingapproach: Canonical/semantic models
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Streaming / event-driven systems
industrywish: Data modelling and quality
region: United States / Canada

survey entry 848
type: SurveyResponse
date created: 2026-01-02T10:41:17.621
industry: Public sector / Education
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: Tools are cattle, not pets. We shouldn't love them. If they've outgrown their usefulness, time to move on
region: United States / Canada

survey entry 849
type: SurveyResponse
date created: 2026-01-02T10:41:49.681
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Mixed/hybrid
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
industrywish: For better data quality, we need business team’s help, otherwise even AI or more engineers won’t be of that much help
region: Latin America

survey entry 850
type: SurveyResponse
date created: 2026-01-02T10:43:55.688
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 10,000+
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Databricks
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Using AI for tactical tasks
modelingapproach: One Big Table
modelingpainpoints: None / modeling is going well
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Career growth / leadership
industrywish: Most of the work can be software engineering. Software engineering tools like domain-driven design (to model more precisely the whole data governance processed and expectation of data products both for producers and consumers) and design patterns (hexagonal architecture, separation of concerns -business domain, application environment-, testability thanks to dependence inversion principles...) to reflect the process modelisation into the code in a clean and robust way. All starts with a good organization, a model of what is expected from business and other "parties prenantes" then a code that reflects all of that.
region: Europe (EU / UK)

survey entry 851
type: SurveyResponse
date created: 2026-01-02T10:44:28.921
industry: Finance
job title: AI Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Middle East Africa
role: AI Engineer
orgsize: 1,000–10,000
teamfocus: Data quality / reliability, ML / AI, Fighting fires
storageenvironment: On-prem data warehouse
orchestration: No orchestration / ad-hoc
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Reliability engineering
region: Middle East / Africa

survey entry 852
type: SurveyResponse
date created: 2026-01-02T10:45:48.748
industry: Tech
job title: Business Intelligence Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Business Intelligence Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Internal company based tools
aiusagefrequency: Weekly
aihelpswith: Pipeline debugging, Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: Career growth / leadership
region: Asia–Pacific

survey entry 853
type: SurveyResponse
date created: 2026-01-02T10:47:49.640
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Infrastructure / platform work, Analytics / BI, ML / AI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Data modeling, Architecture design, Governance / quality checks
aiadoption: AI embedded in most workflows
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Tools don’t support good modeling
architecturetrend: Event-driven architecture
biggestbottleneck: Tool complexity
teamgrowth2026: Grow
educationtopic: All
region: United States / Canada

survey entry 854
type: SurveyResponse
date created: 2026-01-02T10:49:21.730
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: < 50 employees
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: None / modeling is going well
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 855
type: SurveyResponse
date created: 2026-01-02T10:50:30.252
industry: Healthcare
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Architect
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data quality / reliability, Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Architecture design, Governance / quality checks
aiadoption: No meaningful adoption yet
modelingapproach: Mixed (depends on use case)
modelingpainpoints: None / modeling is going well
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
industrywish: That data architects are not data engineers.
region: United States / Canada

survey entry 856
type: SurveyResponse
date created: 2026-01-02T10:51:27.350
industry: Tech
job title: CTO
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: CTO
orgsize: 50–199
teamfocus: Data quality / reliability, ML / AI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Architecture design, Governance / quality checks
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Tools don’t support good modeling
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Context Engineering
industrywish: It's not a fading skill
region: United States / Canada

survey entry 857
type: SurveyResponse
date created: 2026-01-02T10:52:09.064
industry: Healthcare
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 50–199
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Experimenting
modelingapproach: Data Vault
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Career growth / leadership
region: United States / Canada

survey entry 858
type: SurveyResponse
date created: 2026-01-02T10:58:15.648
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Analytics / BI, Fighting fires
storageenvironment: Mixed/hybrid
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Architecture design
aiadoption: No meaningful adoption yet
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 859
type: SurveyResponse
date created: 2026-01-02T10:59:24.042
industry: Tech
job title: Business Intelligence Consultant
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Business Intelligence Consultant
orgsize: 50–199
teamfocus: Ingestion / pipelines, Data modeling / transformation, Analytics / BI, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Data modeling, Architecture design
aiadoption: AI embedded in most workflows
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: None / modeling is going well
architecturetrend: Lakehouse
biggestbottleneck: Compute costs
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
region: Europe (EU / UK)

survey entry 860
type: SurveyResponse
date created: 2026-01-02T11:01:07.089
industry: Public sector / Education
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 200–999
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: No meaningful adoption yet
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership
architecturetrend: Centralized warehouse
biggestbottleneck: Ambiguous ownership
teamgrowth2026: Stay the same
educationtopic: Career growth / leadership
region: United States / Canada

survey entry 861
type: SurveyResponse
date created: 2026-01-02T11:01:11.662
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 10,000+
teamfocus: Data modeling / transformation, Analytics / BI, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: United States / Canada

survey entry 862
type: SurveyResponse
date created: 2026-01-02T11:03:14.431
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Analytics / BI
storageenvironment: On-prem data warehouse
orchestration: UAC
aiusagefrequency: Rarely
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: No meaningful adoption yet
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: Asia–Pacific

survey entry 863
type: SurveyResponse
date created: 2026-01-02T11:05:18.414
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Compute costs
teamgrowth2026: Grow
educationtopic: Streaming / event-driven systems
region: Europe (EU / UK)

survey entry 864
type: SurveyResponse
date created: 2026-01-02T11:12:10.129
industry: Finance
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 200–999
teamfocus: Data modeling / transformation, Analytics / BI, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Governance / quality checks
aiadoption: No meaningful adoption yet
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
region: Europe (EU / UK)

survey entry 865
type: SurveyResponse
date created: 2026-01-02T11:13:47.326
industry: Finance
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 10,000+
teamfocus: Infrastructure / platform work, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery, Architecture design, Governance / quality checks
aiadoption: Using AI for tactical tasks
modelingapproach: One Big Table
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Reliability engineering
region: United States / Canada

survey entry 866
type: SurveyResponse
date created: 2026-01-02T11:19:44.059
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: < 50 employees
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Dagster
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Tools don’t support good modeling
architecturetrend: Centralized warehouse
biggestbottleneck: we are consulting company, so all of the above can be found a each company.
teamgrowth2026: Grow
educationtopic: Architecture patterns
industrywish: That tools do not solve your issues.
region: United States / Canada

survey entry 867
type: SurveyResponse
date created: 2026-01-02T11:26:28.737
industry: Public sector / Education
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 50–199
teamfocus: Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Data modeling, Architecture design
aiadoption: Building internal AI platforms
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership
architecturetrend: Event-driven architecture
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Reliability engineering
region: Europe (EU / UK)

survey entry 868
type: SurveyResponse
date created: 2026-01-02T11:34:48.491
industry: Tech
job title: Software Engineer working on data
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Software Engineer working on data
orgsize: 10,000+
teamfocus: Data quality / reliability, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Compute costs
teamgrowth2026: Not sure
educationtopic: Data Modeling, Streaming, Architecture, Carrer growth
industrywish: I’d like to see more people talking about foundations; more articles and talks about timeless tools. Also, see projects where those foundations were applied and complemented using AI.
region: Europe (EU / UK)

survey entry 869
type: SurveyResponse
date created: 2026-01-02T11:43:34.322
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Dagster
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Career growth / leadership
region: Europe (EU / UK)

survey entry 870
type: SurveyResponse
date created: 2026-01-02T11:44:45.352
industry: Finance
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: One Big Table
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Reliability engineering
industrywish: Difference between analytics engineering and data engineering - specifically the combination of skillsets and outcomes. As an example, I see data (software) engineers hired to build dbt models and analytics (BI) engineers being hired to build bespoke ingestion patterns.
region: Europe (EU / UK)

survey entry 871
type: SurveyResponse
date created: 2026-01-02T11:48:43.541
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data quality / reliability
storageenvironment: Cloud PostgreSQL/MySQL
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Data modeling, Architecture design, Governance / quality checks
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling
architecturetrend: Event-driven architecture
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 872
type: SurveyResponse
date created: 2026-01-02T11:50:54.933
industry: Finance
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Architect
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Fighting fires
storageenvironment: Mixed/hybrid
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery, Data modeling, Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Not sure
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 873
type: SurveyResponse
date created: 2026-01-02T11:51:38.197
industry: Finance
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Shrink
educationtopic: Reliability engineering
industrywish: Data engineering directly support business goals, hence, it's crucial to build solid long term data strategy.
region: Europe (EU / UK)

survey entry 874
type: SurveyResponse
date created: 2026-01-02T11:53:54.982
industry: Healthcare
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Architect
orgsize: 10,000+
teamfocus: Data modeling / transformation, Data quality / reliability, Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling
architecturetrend: Lakehouse
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
industrywish: That it is the most important role in the business...And they need more help and time to do things right, because no one else does anything right.
region: United States / Canada

survey entry 875
type: SurveyResponse
date created: 2026-01-02T11:55:43.096
industry: Healthcare
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 10,000+
teamfocus: Analytics / BI, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Event-driven modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: There’s no magic wand, despite what vendors tell you!
region: United States / Canada

survey entry 876
type: SurveyResponse
date created: 2026-01-02T11:55:48.788
industry: Finance
job title: Business Intelligence Analyst
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Business Intelligence Analyst
orgsize: 1,000–10,000
teamfocus: Analytics / BI, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Data modeling
industrywish: Ownership and lineage have huge ripple effects downstream that are often taken for granted. To use an analogy, whether a river is beautiful and clean or stinks and is full of garbage is whether people upstream can be convinced to stop dumping their trash in the river.
region: United States / Canada

survey entry 877
type: SurveyResponse
date created: 2026-01-02T11:56:13.652
industry: Tech
job title: Practice Lead for Decision Intelligence
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Practice Lead for Decision Intelligence
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”, AI tools produce inconsistent schemas
architecturetrend: Event-driven architecture
biggestbottleneck: Data quality
teamgrowth2026: Grow
educationtopic: AI/LLM integration
industrywish: Understanding the purpose of systems you build is oftentimes more important than using state-of-the-art technology and algorithms.
region: Europe (EU / UK)

survey entry 878
type: SurveyResponse
date created: 2026-01-02T12:04:55.669
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Analytics / BI, Fighting fires
storageenvironment: On-prem data warehouse
orchestration: Dataswarm
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Governance / quality checks
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: United States / Canada

survey entry 879
type: SurveyResponse
date created: 2026-01-02T12:09:39.245
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Rarely
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Data integration
teamgrowth2026: Stay the same
educationtopic: Streaming / event-driven systems
region: United States / Canada

survey entry 880
type: SurveyResponse
date created: 2026-01-02T12:10:42.005
industry: Tech
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: Data Architect
orgsize: 200–999
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Pipeline debugging, Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: AI tools produce inconsistent schemas
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: AI/LLM integration
industrywish: Bad Data; Bad AI
region: Latin America

survey entry 881
type: SurveyResponse
date created: 2026-01-02T12:13:25.359
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Infrastructure / platform work, Fighting fires
storageenvironment: Mixed/hybrid
orchestration: Airflow
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Data modeling
industrywish: Although Data Engineering has a lot in common and lot to learn from Software Engineering - not all practices are applicable in that domain, neither are these disciplines the same.
region: Australia / New Zealand

survey entry 882
type: SurveyResponse
date created: 2026-01-02T12:17:38.813
industry: Finance
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 1,000–10,000
teamfocus: Infrastructure / platform work
storageenvironment: Cloud PostgreSQL/MySQL
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership
architecturetrend: Lakehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 883
type: SurveyResponse
date created: 2026-01-02T12:17:55.700
industry: Retail/e-commerce
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Middle East Africa
role: Analytics Engineer
orgsize: < 50 employees
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Data modeling
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Data quality
teamgrowth2026: Grow
educationtopic: Data modeling
industrywish: Industry Mentor
region: Middle East / Africa

survey entry 884
type: SurveyResponse
date created: 2026-01-02T12:20:11.296
industry: Tech
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Architect
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation, Infrastructure / platform work
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Workfows
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling
architecturetrend: Lakehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 885
type: SurveyResponse
date created: 2026-01-02T12:23:09.814
industry: Healthcare
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Databricks workflows
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Experimenting
modelingapproach: Data Vault
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 886
type: SurveyResponse
date created: 2026-01-02T12:24:11.427
industry: Retail/e-commerce
job title: Software Engineer working on data
sic: this
naics: this
occupational category: this
spatial coverage: Middle East Africa
role: Software Engineer working on data
orgsize: 1,000–10,000
teamfocus: Data quality / reliability, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Data quality
teamgrowth2026: Shrink
educationtopic: Streaming / event-driven systems
region: Middle East / Africa

survey entry 887
type: SurveyResponse
date created: 2026-01-02T12:26:19.288
industry: Healthcare
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Governance / quality checks
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”, AI tools produce inconsistent schemas
architecturetrend: Lakehouse
biggestbottleneck: Data quality
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: Tools and licenses do not solve problems. Data is king. Data Quality first. Domain literacy is equally important for engineers and to focus on why, before the what and the how.
region: United States / Canada

survey entry 888
type: SurveyResponse
date created: 2026-01-02T12:26:26.262
industry: Healthcare
job title: Senior Analyst
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Senior Analyst
orgsize: 1,000–10,000
teamfocus: Data quality / reliability, Fighting fires
storageenvironment: Mixed/hybrid
orchestration: Dagster
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: No meaningful adoption yet
modelingapproach: Mixed (depends on use case)
modelingpainpoints: None / modeling is going well
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Data quality
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: The architecture and tooling is ahead of the use cases. The data is not as useful as people think and the modern data stack is way more advanced than the nature of the business problem. People spend too much on infrastructure without understanding the value of said infrastructure.
region: United States / Canada

survey entry 889
type: SurveyResponse
date created: 2026-01-02T12:27:49.384
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data quality / reliability
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Reliability engineering
industrywish: How role can impact in the organitation and connect with salary
region: Latin America

survey entry 890
type: SurveyResponse
date created: 2026-01-02T12:30:42.319
industry: Manufacturing / Industrial
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Dagster
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Talent / hiring
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 891
type: SurveyResponse
date created: 2026-01-02T12:34:58.336
industry: Manufacturing / Industrial
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Analytics Engineer
orgsize: 10,000+
teamfocus: Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Data modeling, Architecture design, Governance / quality checks
aiadoption: AI embedded in most workflows
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Compute costs
teamgrowth2026: Shrink
educationtopic: AI/LLM integration
industrywish: Need time
region: Asia–Pacific

survey entry 892
type: SurveyResponse
date created: 2026-01-02T12:35:35.845
industry: Retail/e-commerce
job title: Data Intelligence
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Intelligence
orgsize: 10,000+
teamfocus: Analytics / BI
storageenvironment: Salient
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: No meaningful adoption yet
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership
architecturetrend: Centralized warehouse
biggestbottleneck: Data quality
teamgrowth2026: Not sure
educationtopic: Career growth / leadership
region: United States / Canada

survey entry 893
type: SurveyResponse
date created: 2026-01-02T12:36:12.387
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines, Data quality / reliability
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: No meaningful adoption yet
modelingapproach: Canonical/semantic models
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: Streaming / event-driven systems
region: Europe (EU / UK)

survey entry 894
type: SurveyResponse
date created: 2026-01-02T12:36:31.001
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Data quality / reliability, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Data modeling, Governance / quality checks
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Data quality
teamgrowth2026: Grow
educationtopic: Reliability engineering
region: Latin America

survey entry 895
type: SurveyResponse
date created: 2026-01-02T12:36:33.958
industry: Retail/e-commerce
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Data quality / reliability, Analytics / BI, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery, Data modeling
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time
architecturetrend: Centralized warehouse
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 896
type: SurveyResponse
date created: 2026-01-02T12:46:14.070
industry: Healthcare
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 10,000+
teamfocus: Data quality / reliability, Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Architecture patterns
region: Europe (EU / UK)

survey entry 897
type: SurveyResponse
date created: 2026-01-02T12:49:31.420
industry: Tech
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Architect
orgsize: < 50 employees
teamfocus: Data modeling / transformation, ML / AI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Metadata driven frameworks
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Data modeling
aiadoption: Building internal AI platforms
modelingapproach: Data Vault
modelingpainpoints: Tools don’t support good modeling
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Top down ontological modeling
industrywish: Hire experts
region: United States / Canada

survey entry 898
type: SurveyResponse
date created: 2026-01-02T12:50:00.120
industry: Healthcare
job title: Software Engineer working on data
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Software Engineer working on data
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Pipeline debugging, Documentation / data discovery
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: AI/LLM integration
industrywish: It’s incredibly more complicated than traditional web and mobile app engineering.
region: United States / Canada

survey entry 899
type: SurveyResponse
date created: 2026-01-02T12:53:32.180
industry: Manufacturing / Industrial
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: BMC Control-M + Azure Data Factory
aiusagefrequency: Rarely
aihelpswith: I don't find AI helpful
aiadoption: Building internal AI platforms
modelingapproach: Data engineers do not know what data modeling is
modelingpainpoints: Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Shrink
educationtopic: Rewire executive brains
industrywish: That there is no or very little engineering in "data engineering". I would never step on a bridge built like a pipeline...
region: Europe (EU / UK)

survey entry 900
type: SurveyResponse
date created: 2026-01-02T12:55:17.399
industry: Public sector / Education
job title: Business Intelligence
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Business Intelligence
orgsize: 10,000+
teamfocus: Data quality / reliability, Analytics / BI
storageenvironment: Cloud PostgreSQL/MySQL
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Career growth / leadership
region: United States / Canada

survey entry 901
type: SurveyResponse
date created: 2026-01-02T12:56:50.247
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Middle East Africa
role: Manager / Director / VP
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Time and resources
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: Middle East / Africa

survey entry 902
type: SurveyResponse
date created: 2026-01-02T12:58:38.718
industry: Healthcare
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: AI/LLM integration
industrywish: Ai engineering
region: United States / Canada

survey entry 903
type: SurveyResponse
date created: 2026-01-02T13:05:37.306
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: < 50 employees
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 904
type: SurveyResponse
date created: 2026-01-02T13:08:52.446
industry: Public sector / Education
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Data quality / reliability, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Matillion
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: United States / Canada

survey entry 905
type: SurveyResponse
date created: 2026-01-02T13:10:29.905
industry: Retail/e-commerce
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 200–999
teamfocus: Data modeling / transformation, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: One Big Table
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 906
type: SurveyResponse
date created: 2026-01-02T13:15:37.688
industry: Public sector / Education
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 10,000+
teamfocus: Data modeling / transformation, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Either Airflow, Dagster or Prefect (currently evaluating)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: Data Vault
modelingpainpoints: None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
industrywish: The database is not just an implementation detail. What I wish data engineers understood about data engineering: That tight coupling to any technology is limiting the platforms options (so please use appropriate abstractions when interacting with anything out of your control)
region: Europe (EU / UK)

survey entry 907
type: SurveyResponse
date created: 2026-01-02T13:19:51.153
industry: Finance
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Middle East Africa
role: Analytics Engineer
orgsize: 1,000–10,000
teamfocus: Analytics / BI
storageenvironment: Excel Sheets 👀
orchestration: No orchestration / ad-hoc
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Building internal AI platforms
modelingapproach: Event-driven modeling
modelingpainpoints: Pressure to “move fast”, None / modeling is going well
architecturetrend: Event-driven architecture
biggestbottleneck: Compute costs
teamgrowth2026: Grow
educationtopic: Data modeling
industrywish: The importance of data quality
region: Middle East / Africa

survey entry 908
type: SurveyResponse
date created: 2026-01-02T13:33:54.492
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Hard to maintain over time
architecturetrend: Centralized warehouse
biggestbottleneck: Compute costs
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
region: Australia / New Zealand

survey entry 909
type: SurveyResponse
date created: 2026-01-02T13:43:53.800
industry: Tech
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Architect
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: internal
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: None / modeling is going well
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
industrywish: Value data engineering provides to business, there is no direct way of showing impact/metrics in numbers or ROI.
region: United States / Canada

survey entry 910
type: SurveyResponse
date created: 2026-01-02T13:46:49.528
industry: Public sector / Education
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Infrastructure / platform work, Fighting fires
storageenvironment: Mixed/hybrid
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Documentation / data discovery, Governance / quality checks
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: each of Permission to lead
teamgrowth2026: Grow
educationtopic: AI/LLM integration
industrywish: The difference between data architecture for Data analysis and for AI
region: Europe (EU / UK)

survey entry 911
type: SurveyResponse
date created: 2026-01-02T13:52:24.682
industry: Healthcare
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data quality / reliability, Infrastructure / platform work
storageenvironment: Files from public data repository in various file formats etc
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Not sure
educationtopic: Career growth / leadership
industrywish: You need people who are skilled but also willing to up skill with times. Some organisations operates in dinosaur age and it’s a risk to ppl working with those systems, if they are out of job as you suddenly fit nowhere. Catch 22 situation. Burnt myself for being part of this setup. Also if your boss is a dinosaur, your up skill is with useless or a threat to him !
region: Europe (EU / UK)

survey entry 912
type: SurveyResponse
date created: 2026-01-02T13:58:00.703
industry: Tech
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: < 50 employees
teamfocus: Data modeling / transformation, Data quality / reliability, Analytics / BI, ML / AI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Data modeling, Architecture design, Governance / quality checks
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Data modeling
industrywish: There is not analytics/advanced analytics and/or AI/ML work without a proper data foundation, for which data modeling is absolutely critical and not done over night.
region: Europe (EU / UK)

survey entry 913
type: SurveyResponse
date created: 2026-01-02T13:59:32.694
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Data Engineer
orgsize: 50–199
teamfocus: Infrastructure / platform work, Analytics / BI
storageenvironment: Mixed/hybrid
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership
architecturetrend: Lakehouse
biggestbottleneck: Data quality
teamgrowth2026: Grow
educationtopic: Data modeling
industrywish: Importance of data modelling at early stages
region: Australia / New Zealand

survey entry 914
type: SurveyResponse
date created: 2026-01-02T14:04:44.438
industry: Retail/e-commerce
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 200–999
teamfocus: Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Using AI for tactical tasks
modelingapproach: Canonical/semantic models
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: That data is not technical but rather business artifacts. While tech increases stability in pipelines and reduces workload, communication, ownership and governance is, in my experience, where most projects fails.
region: Europe (EU / UK)

survey entry 915
type: SurveyResponse
date created: 2026-01-02T14:07:58.459
industry: Retail/e-commerce
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Manager / Director / VP
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Data modeling, Architecture design
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Compute costs
teamgrowth2026: Grow
educationtopic: Architecture patterns
region: Asia–Pacific

survey entry 916
type: SurveyResponse
date created: 2026-01-02T14:09:03.973
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data quality / reliability
storageenvironment: On-prem data warehouse
orchestration: Dagster
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Centralized warehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Stay the same
educationtopic: Reliability engineering
region: Europe (EU / UK)

survey entry 917
type: SurveyResponse
date created: 2026-01-02T14:10:29.431
industry: Retail/e-commerce
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines, Data modeling / transformation, ML / AI
storageenvironment: On-prem data warehouse
orchestration: No orchestration / ad-hoc
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: AI embedded in most workflows
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Data quality
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 918
type: SurveyResponse
date created: 2026-01-02T14:12:55.088
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 10,000+
teamfocus: Data modeling / transformation
storageenvironment: On-prem data warehouse
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 919
type: SurveyResponse
date created: 2026-01-02T14:15:28.602
industry: Healthcare
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Tools don’t support good modeling
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Data quality
teamgrowth2026: Grow
educationtopic: Architecture patterns
industrywish: Focusing on Business Semantics/Ontologies more than tools
region: Europe (EU / UK)

survey entry 920
type: SurveyResponse
date created: 2026-01-02T14:20:31.011
industry: Healthcare
job title: Data Scientist
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Data Scientist
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Mixed/hybrid
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery, Governance / quality checks
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Centralized warehouse
biggestbottleneck: Data quality
teamgrowth2026: Grow
educationtopic: Architecture patterns
industrywish: That it is foundational to other investments
region: Australia / New Zealand

survey entry 921
type: SurveyResponse
date created: 2026-01-02T14:31:49.928
industry: Healthcare
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: < 50 employees
teamfocus: Analytics / BI, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Temporal
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Event-driven architecture
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Architecture patterns
region: United States / Canada

survey entry 922
type: SurveyResponse
date created: 2026-01-02T14:43:56.509
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: Canonical/semantic models
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Streaming / event-driven systems
region: Europe (EU / UK)

survey entry 923
type: SurveyResponse
date created: 2026-01-02T14:45:55.541
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data modeling / transformation, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: No meaningful adoption yet
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Shrink
educationtopic: Reliability engineering
region: Europe (EU / UK)

survey entry 924
type: SurveyResponse
date created: 2026-01-02T14:46:35.730
industry: Healthcare
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation, Analytics / BI
storageenvironment: On-prem data warehouse
orchestration: Orchestra
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery
aiadoption: No meaningful adoption yet
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Tools don’t support good modeling
architecturetrend: Centralized warehouse
biggestbottleneck: Compute costs
teamgrowth2026: Grow
educationtopic: Career growth / leadership
region: Europe (EU / UK)

survey entry 925
type: SurveyResponse
date created: 2026-01-02T14:49:58.569
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 200–999
teamfocus: Data modeling / transformation, Infrastructure / platform work, Analytics / BI, ML / AI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Governance / quality checks
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Architecture patterns
region: Europe (EU / UK)

survey entry 926
type: SurveyResponse
date created: 2026-01-02T15:06:04.980
industry: Retail/e-commerce
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: DBT Cloud
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Data modeling
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Data modeling
industrywish: Data Modeling
region: Europe (EU / UK)

survey entry 927
type: SurveyResponse
date created: 2026-01-02T15:34:18.375
industry: Manufacturing / Industrial
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Infrastructure / platform work, Fighting fires
storageenvironment: Lakehouse on prem
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery, Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Data quality
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 928
type: SurveyResponse
date created: 2026-01-02T15:36:01.729
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 200–999
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: CircleCI and Snowflake Dynamic Tables
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery, Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: AI/LLM integration
industrywish: Star Schemas are not the answer to everything. Data Modeling is not always synonymous with one single modeling approach.
region: United States / Canada

survey entry 929
type: SurveyResponse
date created: 2026-01-02T15:44:04.483
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Matillion
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 930
type: SurveyResponse
date created: 2026-01-02T15:45:41.601
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 10,000+
teamfocus: Ingestion / pipelines, ML / AI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership
architecturetrend: Lakehouse
biggestbottleneck: Administrative complexity
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: It's more important than data science and data scientists rarely make good data engineers
region: United States / Canada

survey entry 931
type: SurveyResponse
date created: 2026-01-02T15:51:53.648
industry: Public sector / Education
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 10,000+
teamfocus: Analytics / BI, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 932
type: SurveyResponse
date created: 2026-01-02T15:53:16.988
industry: Tech
job title: Data analyst
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data analyst
orgsize: 1,000–10,000
teamfocus: Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Data modeling
aiadoption: No meaningful adoption yet
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
region: Europe (EU / UK)

survey entry 933
type: SurveyResponse
date created: 2026-01-02T15:54:07.804
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability
storageenvironment: On-prem data warehouse
orchestration: Prefect
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 934
type: SurveyResponse
date created: 2026-01-02T16:02:03.425
industry: Manufacturing / Industrial
job title: Data Modeler
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Modeler
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Infrastructure / platform work, Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Data modeling
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Data modeling
industrywish: Investing in data modeling, data quality, semantics, data governance could bring faster ROÍ from data projects and data teams in the long term
region: United States / Canada

survey entry 935
type: SurveyResponse
date created: 2026-01-02T16:02:06.120
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: Analytics Engineer
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Mixed/hybrid
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Career growth / leadership
region: Latin America

survey entry 936
type: SurveyResponse
date created: 2026-01-02T16:14:21.888
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Career growth / leadership
region: Latin America

survey entry 937
type: SurveyResponse
date created: 2026-01-02T16:18:17.494
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 200–999
teamfocus: Fighting fires
storageenvironment: AWS S3 + Glue/Athena
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Data Lake + Power BI and custom reporting tools
biggestbottleneck: Nearly all of the above (except for compute costs and tool complexity)
teamgrowth2026: Stay the same
educationtopic: Data lineage and data contracts
industrywish: Building a solid data infrastructure does not happen overnight, and requires collaboration from the business side. If no one is available to help us understand the crucial human processes at the company, we are not going far. Also, data governance isn’t optional.
region: Europe (EU / UK)

survey entry 938
type: SurveyResponse
date created: 2026-01-02T16:18:52.025
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Infrastructure / platform work, ML / AI, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Canonical/semantic models
modelingpainpoints: Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Streaming / event-driven systems
region: United States / Canada

survey entry 939
type: SurveyResponse
date created: 2026-01-02T16:22:19.188
industry: Public sector / Education
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Manager / Director / VP
orgsize: 50–199
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: On-prem data warehouse
orchestration: In house system
aiusagefrequency: Daily
aihelpswith: Documentation / data discovery, Governance / quality checks
aiadoption: No meaningful adoption yet
modelingapproach: Canonical/semantic models
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Data modeling
industrywish: That it’s not the same as data science!
region: Australia / New Zealand

survey entry 940
type: SurveyResponse
date created: 2026-01-02T16:41:36.859
industry: Finance
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: Analytics Engineer
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Not sure
educationtopic: Architecture patterns
region: Latin America

survey entry 941
type: SurveyResponse
date created: 2026-01-02T16:47:27.389
industry: Finance
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Analytics Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines, Data modeling / transformation, Analytics / BI
storageenvironment: Mixed/hybrid
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: No meaningful adoption yet
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
industrywish: Importance of a definition
region: Australia / New Zealand

survey entry 942
type: SurveyResponse
date created: 2026-01-02T16:48:59.430
industry: Manufacturing / Industrial
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Career growth / leadership
region: United States / Canada

survey entry 943
type: SurveyResponse
date created: 2026-01-02T16:59:03.955
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Streaming / event-driven systems
region: Europe (EU / UK)

survey entry 944
type: SurveyResponse
date created: 2026-01-02T17:08:12.926
industry: Manufacturing / Industrial
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: 10,000+
teamfocus: Data modeling / transformation, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 945
type: SurveyResponse
date created: 2026-01-02T17:18:32.686
industry: Tech
job title: Software Engineer working on data
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Software Engineer working on data
orgsize: 200–999
teamfocus: Infrastructure / platform work, Fighting fires
storageenvironment: Mixed/hybrid
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: AI/LLM integration
industrywish: At scale there’s a high need for creativity especially when building on top of years of historical decisions that you didn’t make.
region: United States / Canada

survey entry 946
type: SurveyResponse
date created: 2026-01-02T17:23:13.886
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Stakeholder validation
teamgrowth2026: Stay the same
educationtopic: Streaming / event-driven systems
region: Latin America

survey entry 947
type: SurveyResponse
date created: 2026-01-02T17:25:50.456
industry: Tech
job title: product data analyst
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: product data analyst
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: mixed
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Shrink
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 948
type: SurveyResponse
date created: 2026-01-02T17:32:11.351
industry: Finance
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Manager / Director / VP
orgsize: 200–999
teamfocus: Data quality / reliability, Fighting fires
storageenvironment: Cloud PostgreSQL/MySQL
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Pipeline debugging, Architecture design
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Shrink
educationtopic: AI/LLM integration
region: Asia–Pacific

survey entry 949
type: SurveyResponse
date created: 2026-01-02T17:57:20.124
industry: Manufacturing / Industrial
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: 200–999
teamfocus: Analytics / BI
storageenvironment: Oracle
orchestration: No orchestration / ad-hoc
aiusagefrequency: Never
aihelpswith: I don't find AI helpful
aiadoption: No meaningful adoption yet
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: None / modeling is going well
architecturetrend: Database
biggestbottleneck: Tool complexity
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: United States / Canada

survey entry 950
type: SurveyResponse
date created: 2026-01-02T18:05:13.789
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Compute costs
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 951
type: SurveyResponse
date created: 2026-01-02T18:11:47.879
industry: Public sector / Education
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Analytics Engineer
orgsize: < 50 employees
teamfocus: Infrastructure / platform work, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: No meaningful adoption yet
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: Australia / New Zealand

survey entry 952
type: SurveyResponse
date created: 2026-01-02T18:12:50.399
industry: Retail/e-commerce
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Architect
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: IBM Workload Scheduler
aiusagefrequency: Rarely
aihelpswith: Documentation / data discovery
aiadoption: No meaningful adoption yet
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Data modeling
industrywish: You need good data to do AI and therefore data engineers are more important than ever.
region: United States / Canada

survey entry 953
type: SurveyResponse
date created: 2026-01-02T18:14:54.812
industry: Healthcare
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: 1,000–10,000
teamfocus: Analytics / BI, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Entirely SQL based in Dataform
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 954
type: SurveyResponse
date created: 2026-01-02T18:20:37.785
industry: Retail/e-commerce
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 10,000+
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Dagster
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 955
type: SurveyResponse
date created: 2026-01-02T18:29:27.737
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 50–199
teamfocus: Data quality / reliability, Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Databricks
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling
architecturetrend: Lakehouse
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: United States / Canada

survey entry 956
type: SurveyResponse
date created: 2026-01-02T18:37:26.164
industry: Public sector / Education
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 10,000+
teamfocus: Infrastructure / platform work, ML / AI
storageenvironment: On-prem data warehouse
orchestration: Dagster
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: Streaming / event-driven systems
industrywish: Our responsibilities dont fall into a clear box. Especially in government/ smaller organizations
region: United States / Canada

survey entry 957
type: SurveyResponse
date created: 2026-01-02T18:44:29.984
industry: Public sector / Education
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Analytics Engineer
orgsize: < 50 employees
teamfocus: Data modeling / transformation
storageenvironment: Mixed/hybrid
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Data modeling
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: Asia–Pacific

survey entry 958
type: SurveyResponse
date created: 2026-01-02T19:18:24.443
industry: Finance
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 10,000+
teamfocus: Analytics / BI, Fighting fires
storageenvironment: On-prem data warehouse
orchestration: No orchestration / ad-hoc
aiusagefrequency: Weekly
aihelpswith: Documentation / data discovery, Architecture design, I don't find AI helpful
aiadoption: Building internal AI platforms
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 959
type: SurveyResponse
date created: 2026-01-02T19:19:57.593
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Infrastructure / platform work, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Tool complexity
teamgrowth2026: Not sure
educationtopic: data credibility (veracity to build trust) / create stable software (no more bugs please)
industrywish: Not everything is to be builded over Spark. Some apps do not require new buzzwords oruse the cutting edge technolgy. Also, current architects do not understand the basics of kappa and lambda architectures. Stop thinking about AI is the solution, stop the hype and create stable sowtware that really works (stop thinking "build that now, latter we will smash those bugs" ignoring the technical debt in the mature stages)
region: Latin America

survey entry 960
type: SurveyResponse
date created: 2026-01-02T19:21:21.321
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Data quality / reliability, Analytics / BI, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Career growth / leadership
region: United States / Canada

survey entry 961
type: SurveyResponse
date created: 2026-01-02T19:22:52.892
industry: Tech
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: < 50 employees
teamfocus: Data modeling / transformation, Infrastructure / platform work
storageenvironment: Cloud PostgreSQL/MySQL
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Pipeline debugging, Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Pressure to “move fast”
architecturetrend: Event-driven architecture
biggestbottleneck: Data quality
teamgrowth2026: Grow
educationtopic: Translating infrastructure value to non-technical stakeholders (aka: How not to be a data apologist)
industrywish: Data engineering is infrastructure: when it works, it’s invisible; when it’s judged, it’s often through failing applications. Good pipelines, schemas, and governance aren’t perceptible as features to most business users, but there should be no doubt that they are non-negotiable prerequisites for any applications, analytics, and/or AI capabilities to succeed at all.
region: Europe (EU / UK)

survey entry 962
type: SurveyResponse
date created: 2026-01-02T19:56:13.699
industry: Finance
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Manager / Director / VP
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation, ML / AI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery, Architecture design
aiadoption: Building internal AI platforms
modelingapproach: Event-driven modeling
modelingpainpoints: Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: Asia–Pacific

survey entry 963
type: SurveyResponse
date created: 2026-01-02T20:20:24.536
industry: Tech
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Architect
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Analytics / BI, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Lakeflow, adf
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Data modeling, Architecture design, Governance / quality checks
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”, AI tools produce inconsistent schemas
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Architecture patterns
industrywish: It takes time to build good and reliable data systems
region: Asia–Pacific

survey entry 964
type: SurveyResponse
date created: 2026-01-02T20:20:57.461
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Weekly
aihelpswith: Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, None / modeling is going well
architecturetrend: Lakehouse
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: Latin America

survey entry 965
type: SurveyResponse
date created: 2026-01-02T20:24:28.730
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery
aiadoption: AI embedded in most workflows
modelingapproach: Data Vault
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling
architecturetrend: Event-driven architecture
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: Asia–Pacific

survey entry 966
type: SurveyResponse
date created: 2026-01-02T20:44:05.023
industry: Finance
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 200–999
teamfocus: Ingestion / pipelines, Fighting fires
storageenvironment: On-prem data warehouse
orchestration: Mainframe based scheduler
aiusagefrequency: Rarely
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: No meaningful adoption yet
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: Data Engineering can't fix business process and data stewardship issues.
region: United States / Canada

survey entry 967
type: SurveyResponse
date created: 2026-01-02T21:22:12.920
industry: Public sector / Education
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 968
type: SurveyResponse
date created: 2026-01-02T21:25:15.211
industry: Retail/e-commerce
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Analytics Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines
storageenvironment: On-prem data warehouse
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Tools don’t support good modeling
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: Asia–Pacific

survey entry 969
type: SurveyResponse
date created: 2026-01-02T21:36:50.854
industry: Retail/e-commerce
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Analytics Engineer
orgsize: 200–999
teamfocus: Data modeling / transformation, Data quality / reliability, ML / AI, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Data modeling, Architecture design, Governance / quality checks
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Compute costs
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: Asia–Pacific

survey entry 970
type: SurveyResponse
date created: 2026-01-02T21:41:41.838
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: luigi
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Data modeling, Architecture design
aiadoption: Building internal AI platforms
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Tools don’t support good modeling
architecturetrend: Lakehouse
biggestbottleneck: Tool complexity
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 971
type: SurveyResponse
date created: 2026-01-02T21:56:26.630
industry: Finance
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data quality / reliability, Analytics / BI, Fighting fires
storageenvironment: Microsoft Fabric
orchestration: Microsoft Fabric
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Architecture design
aiadoption: Building internal AI platforms
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Career growth / leadership
region: Europe (EU / UK)

survey entry 972
type: SurveyResponse
date created: 2026-01-02T21:59:53.558
industry: Finance
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Architect
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Fighting fires
storageenvironment: On prem Cloudera stack + Spark
orchestration: Airflow
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Building internal AI platforms
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Career growth / leadership
region: United States / Canada

survey entry 973
type: SurveyResponse
date created: 2026-01-02T22:12:37.865
industry: Healthcare
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Engineer
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data quality / reliability, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery
aiadoption: No meaningful adoption yet
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Stay the same
educationtopic: Career growth / leadership
region: Asia–Pacific

survey entry 974
type: SurveyResponse
date created: 2026-01-02T22:19:36.347
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data quality / reliability, Fighting fires
storageenvironment: Cloud PostgreSQL/MySQL
orchestration: Custom python
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: No meaningful adoption yet
modelingapproach: Canonical/semantic models
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: AI/LLM integration
industrywish: Pain of adopting industry accepted data model will pay off long term vs. build stuff in house from scratch.
region: United States / Canada

survey entry 975
type: SurveyResponse
date created: 2026-01-02T22:40:40.753
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: On-prem data warehouse
orchestration: Homegrown
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Tools don’t support good modeling
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 976
type: SurveyResponse
date created: 2026-01-02T22:41:57.654
industry: Finance
job title: Data Scientist
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Scientist
orgsize: 10,000+
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Architecture design, Governance / quality checks
aiadoption: No meaningful adoption yet
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: Data Engineering is adjacent but not equivalent to Information Technology (IT)
region: Asia–Pacific

survey entry 977
type: SurveyResponse
date created: 2026-01-02T23:02:24.136
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Infrastructure / platform work, Fighting fires
storageenvironment: Mixed/hybrid
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Not sure
educationtopic: Architecture patterns
region: Asia–Pacific

survey entry 978
type: SurveyResponse
date created: 2026-01-02T23:38:59.762
industry: Tech
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Data modeling
aiadoption: Building internal AI platforms
modelingapproach: Data Vault
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Not sure
educationtopic: Architecture patterns
industrywish: It's not just moving the data from one place to another, after "touching" the data you feel responsible for the quality and reliability of the output, and most of the leadership ignores this.
region: Europe (EU / UK)

survey entry 979
type: SurveyResponse
date created: 2026-01-02T23:49:03.446
industry: Manufacturing / Industrial
job title: ML Engineer / MLOps
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: ML Engineer / MLOps
orgsize: < 50 employees
teamfocus: ML / AI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 980
type: SurveyResponse
date created: 2026-01-02T23:53:40.884
industry: Finance
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Analytics Engineer
orgsize: 50–199
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Data modeling
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Architecture patterns
industrywish: It is the foundation of good analytics and decision making
region: Australia / New Zealand

survey entry 981
type: SurveyResponse
date created: 2026-01-03T00:03:57.349
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Never
aihelpswith: I don't find AI helpful
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership
architecturetrend: Centralized warehouse
biggestbottleneck: Tool complexity
teamgrowth2026: Grow
educationtopic: Reliability engineering
region: Australia / New Zealand

survey entry 982
type: SurveyResponse
date created: 2026-01-03T00:15:41.904
industry: Tech
job title: Software Engineer working on data
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Software Engineer working on data
orgsize: 50–199
teamfocus: Ingestion / pipelines, Data quality / reliability
storageenvironment: Mixed/hybrid
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: AI embedded in most workflows
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: Australia / New Zealand

survey entry 983
type: SurveyResponse
date created: 2026-01-03T00:19:02.350
industry: Healthcare
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 1,000–10,000
teamfocus: Data quality / reliability, Fighting fires
storageenvironment: None, we are developing a data platform
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: We are developing data platform, with hopes to apply federated ownership
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Architecture patterns
industrywish: Data engineering has very low visibility, but high impact
region: Europe (EU / UK)

survey entry 984
type: SurveyResponse
date created: 2026-01-03T00:45:49.371
industry: Tech
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 200–999
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Compute costs
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: Europe (EU / UK)

survey entry 985
type: SurveyResponse
date created: 2026-01-03T00:54:15.759
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Weekly
aihelpswith: Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Data modeling
industrywish: Importance of governance/ownership
region: Europe (EU / UK)

survey entry 986
type: SurveyResponse
date created: 2026-01-03T00:58:44.790
industry: Manufacturing / Industrial
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Analytics / BI, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: One Big Table
modelingpainpoints: Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Reliability engineering
region: Europe (EU / UK)

survey entry 987
type: SurveyResponse
date created: 2026-01-03T01:13:25.877
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: No meaningful adoption yet
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Streaming / event-driven systems
industrywish: We spend a lot of time on things that do not shine, digging in data and see why we got a not from source in a column, and not even the source does not know about it
region: Europe (EU / UK)

survey entry 988
type: SurveyResponse
date created: 2026-01-03T01:31:38.320
industry: Retail/e-commerce
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Building internal AI platforms
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Reliability engineering
region: Europe (EU / UK)

survey entry 989
type: SurveyResponse
date created: 2026-01-03T01:56:59.676
industry: Retail/e-commerce
job title: Data Analyst
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Analyst
orgsize: 200–999
teamfocus: Data quality / reliability, Analytics / BI
storageenvironment: PowerBI
orchestration: PowerBI scheduling
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Governance / quality checks
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Not sure
educationtopic: Reliability engineering
region: Europe (EU / UK)

survey entry 990
type: SurveyResponse
date created: 2026-01-03T02:36:43.279
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Weekly
aihelpswith: Pipeline debugging, Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: AI/LLM integration
industrywish: Data Quality and business perspective
region: Asia–Pacific

survey entry 991
type: SurveyResponse
date created: 2026-01-03T02:39:59.985
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Lakeflow
aiusagefrequency: Weekly
aihelpswith: Pipeline debugging, Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Canonical/semantic models
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
industrywish: Data analytics team should be included right after the ideation process of a product whose analytics will be required. Don't expect us to magically present out of the box analytics for products or businesses you just told us about while excluding us from all previous workshops/brainstorming sessions. Sorry for ranting.
region: Asia–Pacific

survey entry 992
type: SurveyResponse
date created: 2026-01-03T02:42:51.645
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Data quality
teamgrowth2026: Grow
educationtopic: Architecture patterns
region: Europe (EU / UK)

survey entry 993
type: SurveyResponse
date created: 2026-01-03T03:02:44.083
industry: Finance
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 50–199
teamfocus: Data quality / reliability, Fighting fires
storageenvironment: Cloud PostgreSQL/MySQL
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: No meaningful adoption yet
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 994
type: SurveyResponse
date created: 2026-01-03T03:08:47.298
industry: Finance
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Rarely
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: No meaningful adoption yet
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”, AI tools produce inconsistent schemas
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
industrywish: Data architecture / data engineering foundation is a non negotiable and cannot be replaced by fancy AI
region: Europe (EU / UK)

survey entry 995
type: SurveyResponse
date created: 2026-01-03T03:33:36.201
industry: Tech
job title: AI Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: AI Engineer
orgsize: 50–199
teamfocus: Analytics / BI, ML / AI
storageenvironment: Cloud PostgreSQL/MySQL
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Building internal AI platforms
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: Europe (EU / UK)

survey entry 996
type: SurveyResponse
date created: 2026-01-03T03:45:02.498
industry: Public sector / Education
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data quality / reliability, Infrastructure / platform work, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Pipeline debugging
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Project dependent - consultancy based
biggestbottleneck: Talent + extremely poor requirements causing refactoring, delays, etc
teamgrowth2026: Grow
educationtopic: Architecture patterns
industrywish: For my role, cloud understanding is the most important. Which includes lakehouse, semantic modelling, when to use a specific tool and why. Eg streaming data with checkpointing and idempotency checks vs. Batch to send data to a storage account. Cloud is the hardest exp to gain as typically the user has to front the cost to learn these tools
region: Europe (EU / UK)

survey entry 997
type: SurveyResponse
date created: 2026-01-03T03:51:46.034
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Infrastructure / platform work, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Not sure
educationtopic: Career growth / leadership
industrywish: Keep it simple, master fundamentals of data eng'
region: Europe (EU / UK)

survey entry 998
type: SurveyResponse
date created: 2026-01-03T04:12:08.647
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Mixed/hybrid
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: No meaningful adoption yet
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 999
type: SurveyResponse
date created: 2026-01-03T04:22:50.765
industry: Retail/e-commerce
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Analytics Engineer
orgsize: 200–999
teamfocus: Data quality / reliability, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Using AI for tactical tasks
modelingapproach: One Big Table
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: Reliability engineering
industrywish: Data Engineering tasks are not set and forget.
region: Asia–Pacific

survey entry 1000
type: SurveyResponse
date created: 2026-01-03T04:38:25.155
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Dagster
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Building internal AI platforms
modelingapproach: Data Vault
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 1001
type: SurveyResponse
date created: 2026-01-03T04:47:55.576
industry: Public sector / Education
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Tools don’t support good modeling
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
region: Europe (EU / UK)

survey entry 1002
type: SurveyResponse
date created: 2026-01-03T04:50:19.604
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 10,000+
teamfocus: ML / AI, Fighting fires
storageenvironment: On-prem data warehouse
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: The value of good foundation data model and what is it look like.
region: United States / Canada

survey entry 1003
type: SurveyResponse
date created: 2026-01-03T04:55:22.573
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 50–199
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Data modeling
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: Europe (EU / UK)

survey entry 1004
type: SurveyResponse
date created: 2026-01-03T05:28:32.712
industry: Healthcare
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Analytics / BI, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 1005
type: SurveyResponse
date created: 2026-01-03T05:36:03.662
industry: Public sector / Education
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Infrastructure / platform work, Fighting fires
storageenvironment: Mixed/hybrid
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Architecture design
aiadoption: No meaningful adoption yet
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Not sure
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: How much of data engineering work is actually building relationships and convincing people to trust you.
region: United States / Canada

survey entry 1006
type: SurveyResponse
date created: 2026-01-03T05:52:13.254
industry: Healthcare
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Databricks Lakeflow
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Untechnical leadership
teamgrowth2026: Grow
educationtopic: Reliability engineering
industrywish: Continuous improvement means continuos work, there is no "one-off and done" in data
region: Europe (EU / UK)

survey entry 1007
type: SurveyResponse
date created: 2026-01-03T06:14:06.920
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Engineer
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Analytics / BI
storageenvironment: Mixed/hybrid
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: AI embedded in most workflows
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Compute costs
teamgrowth2026: Grow
educationtopic: Streaming / event-driven systems
industrywish: The importance of data quality over speed. Rushing to deliver often creates technical debt.
region: Asia–Pacific

survey entry 1008
type: SurveyResponse
date created: 2026-01-03T06:35:21.361
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Middle East Africa
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Infrastructure / platform work
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: Middle East / Africa

survey entry 1009
type: SurveyResponse
date created: 2026-01-03T06:43:29.944
industry: Healthcare
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 50–199
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: Data Vault
modelingpainpoints: None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 1010
type: SurveyResponse
date created: 2026-01-03T06:47:33.076
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 50–199
teamfocus: Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Streaming / event-driven systems
region: Europe (EU / UK)

survey entry 1011
type: SurveyResponse
date created: 2026-01-03T07:11:28.316
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: Europe (EU / UK)

survey entry 1012
type: SurveyResponse
date created: 2026-01-03T08:32:54.306
industry: Finance
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Data quality / reliability, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Governance / quality checks
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Data modeling
region: United States / Canada

survey entry 1013
type: SurveyResponse
date created: 2026-01-03T10:07:13.224
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: Data quality
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 1014
type: SurveyResponse
date created: 2026-01-03T10:43:07.253
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation, Analytics / BI, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Data modeling, Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Career growth / leadership
region: Asia–Pacific

survey entry 1015
type: SurveyResponse
date created: 2026-01-03T11:11:50.038
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Mixed/hybrid
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Tools don’t support good modeling
architecturetrend: Centralized warehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: Europe (EU / UK)

survey entry 1016
type: SurveyResponse
date created: 2026-01-03T11:15:31.783
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation, Infrastructure / platform work, ML / AI, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Data modeling
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Streaming / event-driven systems
region: United States / Canada

survey entry 1017
type: SurveyResponse
date created: 2026-01-03T11:59:47.026
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Databricks + on-prem DW + data marts (i.e., mixed)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Weekly
aihelpswith: Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: AI/LLM integration
industrywish: Having a semantic understanding of the data is immensely helpful
region: United States / Canada

survey entry 1018
type: SurveyResponse
date created: 2026-01-03T12:15:28.225
industry: Healthcare
job title: Software Engineer working on data
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Software Engineer working on data
orgsize: 1,000–10,000
teamfocus: Analytics / BI, Fighting fires
storageenvironment: Mixed/hybrid
orchestration: No orchestration / ad-hoc
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Not sure
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 1019
type: SurveyResponse
date created: 2026-01-03T12:20:24.858
industry: Tech
job title: Software Engineer working on data
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Software Engineer working on data
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: None / modeling is going well
architecturetrend: Lakehouse
biggestbottleneck: Tool complexity
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: Europe (EU / UK)

survey entry 1020
type: SurveyResponse
date created: 2026-01-03T12:22:13.468
industry: Tech
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Architect
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation, Analytics / BI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Databricks
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Data quality
teamgrowth2026: Grow
educationtopic: Streaming / event-driven systems
industrywish: Data Ownership / Contracts / Data Quality
region: United States / Canada

survey entry 1021
type: SurveyResponse
date created: 2026-01-03T12:31:10.780
industry: Retail/e-commerce
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Middle East Africa
role: Analytics Engineer
orgsize: 10,000+
teamfocus: Data quality / reliability, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Data modeling
region: Middle East / Africa

survey entry 1022
type: SurveyResponse
date created: 2026-01-03T14:14:35.719
industry: Finance
job title: Data Architecture and Engineering consultant
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Architecture and Engineering consultant
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Not sure
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 1023
type: SurveyResponse
date created: 2026-01-03T14:15:08.035
industry: Healthcare
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 50–199
teamfocus: Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: GitLab Pipelines
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Canonical/semantic models
modelingpainpoints: Tools don’t support good modeling
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 1024
type: SurveyResponse
date created: 2026-01-03T14:31:23.272
industry: Healthcare
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data quality / reliability, Infrastructure / platform work
storageenvironment: Mixed/hybrid
orchestration: No orchestration / ad-hoc
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Tools don’t support good modeling
architecturetrend: Centralized warehouse
biggestbottleneck: Tool complexity
teamgrowth2026: Stay the same
educationtopic: Career growth / leadership
region: United States / Canada

survey entry 1025
type: SurveyResponse
date created: 2026-01-03T15:41:33.000
industry: Public sector / Education
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Analytics Engineer
orgsize: 10,000+
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Pipeline debugging, Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: None / modeling is going well
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Onboarding new domain teams effectively into our data mesh network - ensuring appropriate skill level and consistent is maintained
teamgrowth2026: Stay the same
educationtopic: Reliability engineering
industrywish: Fundamentals of data modelling!
region: Australia / New Zealand

survey entry 1026
type: SurveyResponse
date created: 2026-01-03T17:23:30.668
industry: Tech
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Architect
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery, Architecture design
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 1027
type: SurveyResponse
date created: 2026-01-03T20:27:24.178
industry: Tech
job title: Software Engineer working on data
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Software Engineer working on data
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Mixed/hybrid
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: None / modeling is going well
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: Asia–Pacific

survey entry 1028
type: SurveyResponse
date created: 2026-01-03T21:54:52.901
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Infrastructure / platform work, Analytics / BI, ML / AI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Pipeline debugging, Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Tools don’t support good modeling, None / modeling is going well
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Data quality
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 1029
type: SurveyResponse
date created: 2026-01-03T22:01:50.798
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 50–199
teamfocus: Data quality / reliability, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Dagster
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Streaming / event-driven systems
industrywish: There is difference between data engineering, data analytics and data science.
region: United States / Canada

survey entry 1030
type: SurveyResponse
date created: 2026-01-03T22:04:14.807
industry: Tech
job title: ML Engineer / MLOps
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: ML Engineer / MLOps
orgsize: 50–199
teamfocus: Data modeling / transformation, Infrastructure / platform work, ML / AI
storageenvironment: ClickHouse
orchestration: Dagster
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: AI embedded in most workflows
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: None / modeling is going well
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: Australia / New Zealand

survey entry 1031
type: SurveyResponse
date created: 2026-01-04T00:05:38.456
industry: Retail/e-commerce
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Data modeling, Architecture design, Governance / quality checks
aiadoption: Using AI for tactical tasks
modelingapproach: Data Vault
modelingpainpoints: Lack of clear ownership, Tools don’t support good modeling
architecturetrend: Event-driven architecture
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Shrink
educationtopic: Semantics / ontologies / knowledge graphs
region: Europe (EU / UK)

survey entry 1032
type: SurveyResponse
date created: 2026-01-04T01:28:34.287
industry: Tech
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Architect
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Data modeling, Governance / quality checks
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: United States / Canada

survey entry 1033
type: SurveyResponse
date created: 2026-01-04T01:59:09.852
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 200–999
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Streaming / event-driven systems
region: Europe (EU / UK)

survey entry 1034
type: SurveyResponse
date created: 2026-01-04T02:27:41.835
industry: Manufacturing / Industrial
job title: data governance lead
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: data governance lead
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Data quality / reliability
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Documentation / data discovery, Governance / quality checks
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: That consistent data engineering practices is how we operationalize data governance
region: Europe (EU / UK)

survey entry 1035
type: SurveyResponse
date created: 2026-01-04T03:02:48.606
industry: Manufacturing / Industrial
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data quality / reliability, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Canonical/semantic models
modelingpainpoints: Lack of clear ownership
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Shrink
educationtopic: Reliability engineering
industrywish: Importance of Collaboration required to tackle Data Quality
region: Europe (EU / UK)

survey entry 1036
type: SurveyResponse
date created: 2026-01-04T04:33:27.177
industry: Tech
job title: Sales Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Sales Engineer
orgsize: 10,000+
teamfocus: Data quality / reliability, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Actian DataConnect
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: Streaming / event-driven systems
region: Europe (EU / UK)

survey entry 1037
type: SurveyResponse
date created: 2026-01-04T06:27:01.524
industry: Finance
job title: Data Scientist
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Scientist
orgsize: 10,000+
teamfocus: Data modeling / transformation, Data quality / reliability, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Rarely
aihelpswith: Documentation / data discovery, I don't find AI helpful
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of pipeline automation
teamgrowth2026: Grow
educationtopic: Data modeling
industrywish: Loads of manual labor are used in place of design and discipline.
region: United States / Canada

survey entry 1038
type: SurveyResponse
date created: 2026-01-04T07:13:52.075
industry: Finance
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Middle East Africa
role: Manager / Director / VP
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation, Analytics / BI
storageenvironment: On-prem data warehouse
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Documentation / data discovery, Governance / quality checks
aiadoption: Building internal AI platforms
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”, AI tools produce inconsistent schemas, None / modeling is going well
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Not sure
educationtopic: Data modeling
region: Middle East / Africa

survey entry 1039
type: SurveyResponse
date created: 2026-01-04T08:22:05.426
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: ML / AI
storageenvironment: Mixed/hybrid
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Data contracts & Data Products
industrywish: They need to drop adhoc mindset for a strategic approach.
region: United States / Canada

survey entry 1040
type: SurveyResponse
date created: 2026-01-04T08:26:44.673
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 200–999
teamfocus: Analytics / BI, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Event-driven modeling
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Career growth / leadership
region: Europe (EU / UK)

survey entry 1041
type: SurveyResponse
date created: 2026-01-04T08:27:09.662
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Databricks
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
industrywish: Your data is not Big Data
region: Asia–Pacific

survey entry 1042
type: SurveyResponse
date created: 2026-01-04T08:33:15.166
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 200–999
teamfocus: Data modeling / transformation, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
industrywish: Moving from data engineering focusing on analytics to data engineering serving different purposes (shared architecture with integration, modular architecture to serve different needs and refresh frequency)
region: Europe (EU / UK)

survey entry 1043
type: SurveyResponse
date created: 2026-01-04T11:15:54.704
industry: Healthcare
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 50–199
teamfocus: Ingestion / pipelines, Fighting fires
storageenvironment: Mixed/hybrid
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”, None / modeling is going well
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: None really
industrywish: That we aren't miracle workers
region: United States / Canada

survey entry 1044
type: SurveyResponse
date created: 2026-01-04T12:08:54.524
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Shrink
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: The most difficult part about data engineering is getting the business to care about the input as much as the output.
region: United States / Canada

survey entry 1045
type: SurveyResponse
date created: 2026-01-04T15:52:11.239
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Analytics Engineer
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Data modeling
region: United States / Canada

survey entry 1046
type: SurveyResponse
date created: 2026-01-04T16:03:12.417
industry: Tech
job title: Platform Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Middle East Africa
role: Platform Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Infrastructure / platform work, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Azure Data Factory
aiusagefrequency: Daily
aihelpswith: Pipeline debugging, Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Dv2.0 (silver) + Dimensional modelling (gold)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Data quality
teamgrowth2026: Grow
educationtopic: Architecture patterns
industrywish: Data quality from source.
region: Middle East / Africa

survey entry 1047
type: SurveyResponse
date created: 2026-01-04T18:00:57.154
industry: Finance
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Fighting fires
storageenvironment: Mixed/hybrid
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Documentation / data discovery, Governance / quality checks
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 1048
type: SurveyResponse
date created: 2026-01-04T22:45:44.967
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 50–199
teamfocus: Data modeling / transformation, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Dagster
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Data modeling
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Event-driven architecture
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: Europe (EU / UK)

survey entry 1049
type: SurveyResponse
date created: 2026-01-05T01:22:51.269
industry: Finance
job title: Platform Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Middle East Africa
role: Platform Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Infrastructure / platform work
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership
architecturetrend: Lakehouse
biggestbottleneck: Compute costs
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: Middle East / Africa

survey entry 1050
type: SurveyResponse
date created: 2026-01-05T01:34:02.659
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Analytics Engineer
orgsize: 10,000+
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Data modeling
aiadoption: Building internal AI platforms
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: None / modeling is going well
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Shrink
educationtopic: Architecture patterns
region: Asia–Pacific

survey entry 1051
type: SurveyResponse
date created: 2026-01-05T05:05:20.386
industry: Retail/e-commerce
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: < 50 employees
teamfocus: Analytics / BI, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Snowflake Tasks/dbt Cloud
aiusagefrequency: Rarely
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: No meaningful adoption yet
modelingapproach: One Big Table
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: Europe (EU / UK)

survey entry 1052
type: SurveyResponse
date created: 2026-01-05T05:29:35.965
industry: Retail/e-commerce
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 10,000+
teamfocus: Data modeling / transformation, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: I don't find AI helpful
aiadoption: Building internal AI platforms
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Compute costs
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: United States / Canada

survey entry 1053
type: SurveyResponse
date created: 2026-01-05T08:21:13.815
industry: Manufacturing / Industrial
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Architect
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: No meaningful adoption yet
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Not sure
educationtopic: Career growth / leadership
region: United States / Canada

survey entry 1054
type: SurveyResponse
date created: 2026-01-05T15:48:59.530
industry: Public sector / Education
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Data modeling, Architecture design, Governance / quality checks
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: None / modeling is going well
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Not sure
educationtopic: Career growth / leadership
region: United States / Canada

survey entry 1055
type: SurveyResponse
date created: 2026-01-05T15:49:16.911
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 10,000+
teamfocus: Data modeling / transformation, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Dataform
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: AI embedded in most workflows
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Talent / hiring
teamgrowth2026: Stay the same
educationtopic: Streaming / event-driven systems
industrywish: We are at the core of AI, stop building new things in top of shitty data if you don't want to obtain shitty results. Prioritize better, please.
region: Europe (EU / UK)

survey entry 1056
type: SurveyResponse
date created: 2026-01-05T20:26:21.009
industry: Retail/e-commerce
job title: Business Owner
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Business Owner
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Infrastructure / platform work, Analytics / BI, ML / AI, Fighting fires
storageenvironment: On-prem data warehouse
orchestration: No orchestration / ad-hoc
aiusagefrequency: Never
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: No meaningful adoption yet
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Compute costs
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: Asia–Pacific

survey entry 1057
type: SurveyResponse
date created: 2026-01-06T00:13:19.138
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Dagster
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Hard to maintain over time
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Streaming / event-driven systems
region: Europe (EU / UK)

survey entry 1058
type: SurveyResponse
date created: 2026-01-06T01:35:41.587
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud PostgreSQL/MySQL
orchestration: Prefect
aiusagefrequency: Weekly
aihelpswith: Pipeline debugging, I don't find AI helpful
aiadoption: No meaningful adoption yet
modelingapproach: Canonical/semantic models
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Tools don’t support good modeling
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: Semantics, ontologies, knowledge graphs
region: Europe (EU / UK)

survey entry 1059
type: SurveyResponse
date created: 2026-01-06T05:15:29.602
industry: Healthcare
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: ADF
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Compute costs
teamgrowth2026: Grow
educationtopic: Data modeling
industrywish: The value add/benefits of quality data modelling
region: Australia / New Zealand

survey entry 1060
type: SurveyResponse
date created: 2026-01-06T05:21:19.198
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Compute costs
teamgrowth2026: Grow
educationtopic: Streaming / event-driven systems
region: Europe (EU / UK)

survey entry 1061
type: SurveyResponse
date created: 2026-01-06T08:03:38.183
industry: Healthcare
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Infrastructure / platform work
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Databricks Workflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: None / modeling is going well
architecturetrend: Lakehouse
biggestbottleneck: Compute costs
teamgrowth2026: Grow
educationtopic: Career growth / leadership
region: United States / Canada

survey entry 1062
type: SurveyResponse
date created: 2026-01-06T08:23:51.085
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 10,000+
teamfocus: Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Home grown Java batch jobs
aiusagefrequency: Daily
aihelpswith: Documentation / data discovery
aiadoption: No meaningful adoption yet
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 1063
type: SurveyResponse
date created: 2026-01-06T10:12:58.562
industry: Retail/e-commerce
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 200–999
teamfocus: Infrastructure / platform work, ML / AI
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Databricks Workflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: AI/LLM integration
industrywish: Data engineering is software engineering and demands the same engineering rigor (SDLC). Furthermore, data platforms must be treated as internal products, requiring a shift toward genuine product management mindsets.
region: Europe (EU / UK)

survey entry 1064
type: SurveyResponse
date created: 2026-01-06T12:15:05.219
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines
storageenvironment: On-prem data warehouse
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: None / modeling is going well
architecturetrend: Event-driven architecture
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
region: Europe (EU / UK)

survey entry 1065
type: SurveyResponse
date created: 2026-01-06T14:19:14.269
industry: Tech
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Analytics Engineer
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: None / modeling is going well
architecturetrend: Lakehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Stay the same
educationtopic: Data modeling
industrywish: That it's a means to an end
region: Europe (EU / UK)

survey entry 1066
type: SurveyResponse
date created: 2026-01-06T14:42:10.240
industry: Manufacturing / Industrial
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Architect
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Data Vault
modelingpainpoints: None / modeling is going well
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Architecture patterns
region: United States / Canada

survey entry 1067
type: SurveyResponse
date created: 2026-01-06T15:11:43.147
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Databricks Workflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Data modeling
aiadoption: No meaningful adoption yet
modelingapproach: DataVault+kimball
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Architecture patterns
region: Europe (EU / UK)

survey entry 1068
type: SurveyResponse
date created: 2026-01-06T20:18:14.968
industry: Healthcare
job title: Software Engineer working on data
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Software Engineer working on data
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Governance / quality checks
aiadoption: No meaningful adoption yet
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time
architecturetrend: Event-driven architecture
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Shrink
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: That LLMs are not Data Engineers
region: Asia–Pacific

survey entry 1069
type: SurveyResponse
date created: 2026-01-06T21:21:26.265
industry: Manufacturing / Industrial
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 200–999
teamfocus: Ingestion / pipelines, Infrastructure / platform work, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Data modeling, Architecture design
aiadoption: No meaningful adoption yet
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”, AI tools produce inconsistent schemas
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: Data management required components and prerequisites to use your organizations data.
region: United States / Canada

survey entry 1070
type: SurveyResponse
date created: 2026-01-06T23:32:06.768
industry: Public sector / Education
job title: Data Analyst
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Analyst
orgsize: 10,000+
teamfocus: Data quality / reliability, Analytics / BI, Fighting fires
storageenvironment: Mixed/hybrid
orchestration: No orchestration / ad-hoc
aiusagefrequency: Never
aihelpswith: I don't find AI helpful
aiadoption: No meaningful adoption yet
modelingapproach: One Big Table
modelingpainpoints: Lack of clear ownership
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Not sure
educationtopic: AI/LLM integration
industrywish: Data Engineering looks different per each Data Engineer.
region: United States / Canada

survey entry 1071
type: SurveyResponse
date created: 2026-01-06T23:54:28.248
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Data Engineer
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: AI/LLM integration
industrywish: No good analytics without solid data engineering
region: Australia / New Zealand

survey entry 1072
type: SurveyResponse
date created: 2026-01-07T02:47:18.282
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 200–999
teamfocus: Infrastructure / platform work, Fighting fires
storageenvironment: On-prem data warehouse
orchestration: Dagster
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: Europe (EU / UK)

survey entry 1073
type: SurveyResponse
date created: 2026-01-07T03:52:33.141
industry: Tech
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 1,000–10,000
teamfocus: Ingestion / pipelines, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Matillion
aiusagefrequency: Rarely
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Not sure
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: That it is an engineering discipline not application support
region: Europe (EU / UK)

survey entry 1074
type: SurveyResponse
date created: 2026-01-07T04:41:44.589
industry: Public sector / Education
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Latin America
role: Data Architect
orgsize: < 50 employees
teamfocus: Data modeling / transformation, Data quality / reliability, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Governance / quality checks
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
region: Latin America

survey entry 1075
type: SurveyResponse
date created: 2026-01-07T09:31:54.737
industry: Finance
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 10,000+
teamfocus: Data modeling / transformation, Data quality / reliability, Infrastructure / platform work, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Governance / quality checks
aiadoption: No meaningful adoption yet
modelingapproach: Canonical/semantic models
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
region: Europe (EU / UK)

survey entry 1076
type: SurveyResponse
date created: 2026-01-07T10:23:07.428
industry: Manufacturing / Industrial
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Architect
orgsize: 1,000–10,000
teamfocus: Data quality / reliability, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: No meaningful adoption yet
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
region: Europe (EU / UK)

survey entry 1077
type: SurveyResponse
date created: 2026-01-07T12:57:30.494
industry: Tech
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data modeling / transformation
storageenvironment: ClickHouse and DuckDB
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Building internal AI platforms
modelingapproach: One Big Table
modelingpainpoints: Hard to maintain over time
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: That it's hard, thorny work that requires continuous investment to tame the entropy. "Another flaw in the human character is that everybody wants to build and nobody wants to do maintenance.” - Kurt Vonnegut
region: United States / Canada

survey entry 1078
type: SurveyResponse
date created: 2026-01-08T00:42:06.907
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Engineer
orgsize: < 50 employees
teamfocus: Data modeling / transformation
storageenvironment: Microsoft Fabric
orchestration: Fabric(Data Factory)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Data modeling, Architecture design
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Data modeling
industrywish: AI projects as the data engineer is the base line because AI need clean and meaning full data to train.
region: Asia–Pacific

survey entry 1079
type: SurveyResponse
date created: 2026-01-08T01:22:56.303
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Infrastructure / platform work, Analytics / BI, ML / AI, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: AI embedded in most workflows
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Compute costs
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: Europe (EU / UK)

survey entry 1080
type: SurveyResponse
date created: 2026-01-08T01:45:31.492
industry: Public sector / Education
job title: Data Architect
sic: this
naics: this
occupational category: this
spatial coverage: Australia New Zealand
role: Data Architect
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Infrastructure / platform work, Analytics / BI
storageenvironment: On-prem data warehouse
orchestration: Kestra
aiusagefrequency: Rarely
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery
aiadoption: No meaningful adoption yet
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Semantics / ontologies / knowledge graphs
industrywish: Data is not software, data acculturation is mandatory in an enterprise to have a big adoption and a better leadership in strategic, tactical and operational for the wealth care of the company
region: Australia / New Zealand

survey entry 1081
type: SurveyResponse
date created: 2026-01-08T04:08:23.906
industry: Public sector / Education
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 200–999
teamfocus: Ingestion / pipelines
storageenvironment: Mixed/hybrid
orchestration: Mage
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Experimenting
modelingapproach: One Big Table
modelingpainpoints: Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: Data modeling
region: Europe (EU / UK)

survey entry 1082
type: SurveyResponse
date created: 2026-01-08T11:54:00.729
industry: Finance
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 50–199
teamfocus: Data modeling / transformation, Data quality / reliability, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: lambda
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 1083
type: SurveyResponse
date created: 2026-01-09T03:27:39.045
industry: Public sector / Education
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Engineer
orgsize: 200–999
teamfocus: Data quality / reliability
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Governance / quality checks
aiadoption: Using AI for tactical tasks
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: Streaming / event-driven systems
region: Asia–Pacific

survey entry 1084
type: SurveyResponse
date created: 2026-01-09T04:01:44.662
industry: Healthcare
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Engineer
orgsize: 50–199
teamfocus: Data modeling / transformation, Data quality / reliability, Analytics / BI, ML / AI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Canonical/semantic models
modelingpainpoints: None / modeling is going well
architecturetrend: Lakehouse
biggestbottleneck: Talent / hiring
teamgrowth2026: Grow
educationtopic: Data modeling
industrywish: It's important part of data strategy. going forward data engineers will be in high demand
region: Asia–Pacific

survey entry 1085
type: SurveyResponse
date created: 2026-01-09T05:03:06.824
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: No meaningful adoption yet
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Shrink
educationtopic: Data modeling
industrywish: SISO
region: Europe (EU / UK)

survey entry 1086
type: SurveyResponse
date created: 2026-01-09T06:17:41.184
industry: Tech
job title: Software Engineer working on data
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Software Engineer working on data
orgsize: 10,000+
teamfocus: Infrastructure / platform work
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Custom
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Shrink
educationtopic: Streaming / event-driven systems
region: United States / Canada

survey entry 1087
type: SurveyResponse
date created: 2026-01-09T08:47:32.142
industry: Public sector / Education
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Engineer
orgsize: 50–199
teamfocus: Data quality / reliability, Infrastructure / platform work
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Kubernetes Job/Cronjob chains
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Using AI for tactical tasks
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”, AI tools produce inconsistent schemas
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 1088
type: SurveyResponse
date created: 2026-01-10T10:03:33.439
industry: Retail/e-commerce
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 10,000+
teamfocus: Data modeling / transformation
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Jenkins
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc)
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Pressure to “move fast”
architecturetrend: Data mesh / federated ownership
biggestbottleneck: Talent / hiring
teamgrowth2026: Stay the same
educationtopic: Career growth / leadership
region: Europe (EU / UK)

survey entry 1089
type: SurveyResponse
date created: 2026-01-10T10:07:02.587
industry: Retail/e-commerce
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Data Engineer
orgsize: 1,000–10,000
teamfocus: Data modeling / transformation, Data quality / reliability, Analytics / BI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: No meaningful adoption yet
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Pressure to “move fast”
architecturetrend: Depends on the client company and their current data maturity, business use cases and issues they are currently facing
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
industrywish: That it is necessary and dealing with and processing data is not the same as software engineering practices and concepts. They have overlap but in data engineering we have a lot more to consider when it comes to dealing with large-scale data.
region: Asia–Pacific

survey entry 1090
type: SurveyResponse
date created: 2026-01-10T10:24:22.938
industry: Manufacturing / Industrial
job title: Software Engineer working on data
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Software Engineer working on data
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability, Infrastructure / platform work
storageenvironment: Mixed/hybrid
orchestration: No orchestration / ad-hoc
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery, Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Data quality
teamgrowth2026: Stay the same
educationtopic: Architecture patterns
region: United States / Canada

survey entry 1091
type: SurveyResponse
date created: 2026-01-10T10:54:16.403
industry: Manufacturing / Industrial
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: AI/LLM integration
industrywish: AI does not work well without proper data. We are not just plumbers, we can't magically do our work without requirements.
region: Europe (EU / UK)

survey entry 1092
type: SurveyResponse
date created: 2026-01-10T11:17:19.580
industry: Manufacturing / Industrial
job title: Data/ML/AI Consultant [Presales + Implementation Responsibilities]
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data/ML/AI Consultant [Presales + Implementation Responsibilities]
orgsize: 1,000–10,000
teamfocus: ML / AI, Fighting fires
storageenvironment: Lakehouse (Databricks, Iceberg/Hudi/Delta + compute engine)
orchestration: Cloud-native (GCP Cloud Composer, AWS MWAA, etc.)
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: AI embedded in most workflows
modelingapproach: Kimball-style dimensional modeling
modelingpainpoints: Lack of clear ownership
architecturetrend: Lakehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Stay the same
educationtopic: Career growth / leadership
industrywish: Note: I work mostly in AI/ML so the models need to work. So in that light, Data Lineage and importance of your data being able to express the business accurately. There's so much stateless tribal institutional information not captured in the data. Good Data Models, then simple things like understanding how columns of data, even simply it's cardinality for instance, can really make or break the "expression" of that data. We need more thoughtfulness in how we express and capture data in the data models.
region: United States / Canada

survey entry 1093
type: SurveyResponse
date created: 2026-01-10T12:08:30.269
industry: Tech
job title: AI Engineer
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: AI Engineer
orgsize: 10,000+
teamfocus: ML / AI
storageenvironment: Vector Databases - AstroDB + WatsonX Orchestrate Knowledge Base
orchestration: WatsonX Orchestrate + Langflow
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Architecture design
aiadoption: Using AI for tactical tasks
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Grow
educationtopic: Career growth / leadership
region: United States / Canada

survey entry 1094
type: SurveyResponse
date created: 2026-01-10T12:13:11.589
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 50–199
teamfocus: Ingestion / pipelines
storageenvironment: Cloud PostgreSQL/MySQL
orchestration: No orchestration / ad-hoc
aiusagefrequency: Weekly
aihelpswith: Pipeline debugging
aiadoption: Building internal AI platforms
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: Data modeling
industrywish: More time now means less time later
region: Europe (EU / UK)

survey entry 1095
type: SurveyResponse
date created: 2026-01-10T13:16:36.407
industry: Finance
job title: Data Scientist
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Data Scientist
orgsize: 200–999
teamfocus: Analytics / BI, Fighting fires
storageenvironment: On-prem data warehouse
orchestration: SSIS
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: No meaningful adoption yet
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Career growth / leadership
region: United States / Canada

survey entry 1096
type: SurveyResponse
date created: 2026-01-10T14:06:03.719
industry: Finance
job title: Analytics Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Asia Pacific
role: Analytics Engineer
orgsize: 200–999
teamfocus: Analytics / BI, Fighting fires
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: No orchestration / ad-hoc
aiusagefrequency: Weekly
aihelpswith: Documentation / data discovery
aiadoption: Using AI for tactical tasks
modelingapproach: Canonical/semantic models
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Shrink
educationtopic: Career growth / leadership
region: Asia–Pacific

survey entry 1097
type: SurveyResponse
date created: 2026-01-10T20:10:06.185
industry: Finance
job title: Founder of AI platform
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Founder of AI platform
orgsize: < 50 employees
teamfocus: Ingestion / pipelines, ML / AI
storageenvironment: Cloud PostgreSQL/MySQL
orchestration: Langfuse
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: AI embedded in most workflows
modelingapproach: Event-driven modeling
modelingpainpoints: None / modeling is going well
architecturetrend: Event-driven architecture
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Stay the same
educationtopic: Streaming / event-driven systems
region: United States / Canada

survey entry 1098
type: SurveyResponse
date created: 2026-01-10T23:21:39.090
industry: Finance
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: United States Canada
role: Manager / Director / VP
orgsize: 1,000–10,000
teamfocus: Infrastructure / platform work, ML / AI
storageenvironment: Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)
orchestration: Airflow
aiusagefrequency: Weekly
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: Experimenting
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Legacy / technical debt
teamgrowth2026: Grow
educationtopic: AI/LLM integration
region: United States / Canada

survey entry 1099
type: SurveyResponse
date created: 2026-01-11T01:37:39.545
industry: Tech
job title: Data Engineer
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Data Engineer
orgsize: 10,000+
teamfocus: Ingestion / pipelines, Data modeling / transformation, Data quality / reliability
storageenvironment: On-prem data warehouse
orchestration: chronos (internal)
aiusagefrequency: Multiple times per day
aihelpswith: Writing Code (SQL, Python, etc), Pipeline debugging, Documentation / data discovery, Governance / quality checks
aiadoption: Building internal AI platforms
modelingapproach: Mixed (depends on use case)
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Pressure to “move fast”
architecturetrend: Lakehouse
biggestbottleneck: Poor requirements / upstream issues
teamgrowth2026: Stay the same
educationtopic: AI/LLM integration
industrywish: Better you know your data, faster you catch the issues
region: Europe (EU / UK)

survey entry 1100
type: SurveyResponse
date created: 2026-01-11T07:08:54.174
industry: Manufacturing / Industrial
job title: Manager / Director / VP
sic: this
naics: this
occupational category: this
spatial coverage: Europe EU UK
role: Manager / Director / VP
orgsize: 200–999
teamfocus: Ingestion / pipelines, Data quality / reliability, Fighting fires
storageenvironment: On-prem data warehouse
orchestration: Airflow
aiusagefrequency: Daily
aihelpswith: Writing Code (SQL, Python, etc), Documentation / data discovery
aiadoption: No meaningful adoption yet
modelingapproach: Ad-hoc / tables added as needed
modelingpainpoints: Lack of clear ownership, Hard to maintain over time, Tools don’t support good modeling, Pressure to “move fast”
architecturetrend: Centralized warehouse
biggestbottleneck: Lack of leadership direction
teamgrowth2026: Grow
educationtopic: Data modeling
region: Europe (EU / UK)

Tech
type: Industry

Public sector / Education
type: Industry

Manufacturing / Industrial
type: Industry

Finance
type: Industry

Retail/e-commerce
type: Industry

Healthcare
type: Industry

Manager / Director / VP
type: Role

Data Architect
type: Role

Platform Engineer
type: Role

Analytics Engineer
type: Role

BI Engineer
type: Role

Data Engineer
type: Role

Team of 1
type: Role

Analyst
type: Role

Data Analyst / BI Developer
type: Role

early startup founder
type: Role

Software Engineer working on data
type: Role

ML Engineer / MLOps
type: Role

BI LEADER
type: Role

All of above
type: Role

Agile Data Coach
type: Role

CDO
type: Role

Associate
type: Role

Data Analyst
type: Role

Data & AI Engineer
type: Role

BI and Data engineering
type: Role

Product Analysts
type: Role

AI Engineer
type: Role

Founder/CO’s LMW farm
type: Role

Founder
type: Role

Technical Account Manager
type: Role

Operational system designer, which doesnt seem to be a real job.
type: Role

Data governance consultant (vendor)
type: Role

BI Analyst
type: Role

Migration Consultant
type: Role

Report Developer
type: Role

Data Products
type: Role

BI Functional analyst
type: Role

Data analyst
type: Role

Data PM
type: Role

Data Scientist
type: Role

BI Developer
type: Role

Solution Architect
type: Role

MLOps, ML Engineer, Platform Engineer
type: Role

Analyst
type: Role

Nonprofit Director trying to learn data engineering
type: Role

product manager
type: Role

Product manager
type: Role

Data scientist
type: Role

BI Specialist
type: Role

Principal DevRel Engineer
type: Role

Data Business Analyst or Product Owner
type: Role

SRE
type: Role

All of the above
type: Role

Technology Strategist
type: Role

Product Manager
type: Role

Data Modeller
type: Role

Consultant working on migrations and data quality
type: Role

Head of Development, architect
type: Role

Standards developer
type: Role

ML-DL Professor
type: Role

Data Platform Product Manager
type: Role

Data Analyst
type: Role

Data Engineer & Mainframe Developer
type: Role

Knowledge Engineer
type: Role

Data analist
type: Role

Data Scientist
type: Role

Sales engineer
type: Role

Field CTO (Sales)
type: Role

Job seeker
type: Role

Data Product Manager
type: Role

Data & Analytics Engineer
type: Role

Business Intelligence Engineer
type: Role

CTO
type: Role

Business Intelligence Consultant
type: Role

Business Intelligence Analyst
type: Role

Practice Lead for Decision Intelligence
type: Role

Senior Analyst
type: Role

Data Intelligence
type: Role

Business Intelligence
type: Role

Data Modeler
type: Role

product data analyst
type: Role

Data Architecture and Engineering consultant
type: Role

data governance lead
type: Role

Sales Engineer
type: Role

Business Owner
type: Role

Data/ML/AI Consultant [Presales + Implementation Responsibilities]
type: Role

Founder of AI platform
type: Role