Data Engineering
What does the data engineering work chart look like?
Work Chart
| Activity | Human Role | AI Role | AI % | Trend |
|---|---|---|---|---|
| Schema design | Domain modeling, trade-off decisions | Suggests patterns, validates constraints | 40% | ↑ |
| ETL pipelines | Defines sources, validates output | Builds connectors, transforms data | 65% | ↑↑ |
| Data quality | Sets thresholds, reviews anomalies | Monitoring, anomaly detection, cleaning | 60% | ↑ |
| Query optimization | Judgment on access patterns | Index suggestions, query rewriting | 55% | ↑ |
| Documentation | Reviews for accuracy | Generates schema docs, lineage maps | 70% | ↑ |
Roles
| Role | What They Do | Where AI Shifts It |
|---|---|---|
| Data Analyst | Interprets data, builds reports | AI handles exploratory analysis, human focuses on insight |
| Data Engineer | Builds pipelines, maintains infrastructure | AI generates boilerplate, human designs architecture |
| Data Scientist | Models, experiments, predictions | AI runs experiments faster, human frames the right questions |
Context
- Data Footprint — The instrument that measures what's connected
- Platform: Data Engineering — Engineering depth for pipelines
- Work Charts — The framework these activities map to
- Process Types
Questions
Which data engineering activity delivers the most value when automated — and which one breaks fastest without human judgment?
- If AI handles 65% of ETL pipeline work, what does the human's remaining 35% actually look like day to day?
- When schema design decisions compound downstream, how do you catch a bad trade-off before it becomes migration debt?