Automation index
How much of a Data engineer’s job can AI already do?
A pipeline and a diagram sketch. Keeping it up at 3 a.m., with PII and a four-hour job, does not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Data engineer
31%
AI can already do 31% of this job.
#36 / 773
View rankingData engineer
Where the time goes
Task weights are our estimates based on O*NET frequency data, not time shares published by O*NET.
The time saving for each task is estimated separately.
| Task | Share | AI | Points |
|---|---|---|---|
Design pipelines, models and orchestration.EstimateNo public source provides a precise figure. This is our estimate. | 24% | 50% | +12 |
Write SQL, Spark and data tests.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 45% | +8.1 |
Incidents, quality and lineage in production.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Talk to analysts and negotiate data contracts.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Cloud cost, permissions and PII.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 15% | +2.1 |
Decide architecture and technical debt.EstimateNo public source provides a precise figure. This is our estimate. | 12% | 20% | +2.4 |
| Total | 100% | 31% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Design pipelines, models and orchestration
- Write SQL, Spark and data tests
- Cloud cost, permissions and PII
- Decide architecture and technical debt
- ingeniero de datos
- data engineer
- etl
Sources for this job· O*NET 15-1243.00· ESCO/ISCO 2511
IS WORK OVER?
MLOps engineer
27%
#198 / 773
View rankingMLOps engineer
Where the time goes
Task weights are our estimates based on O*NET frequency data, not time shares published by O*NET.
The time saving for each task is estimated separately.
| Task | Share | AI | Points |
|---|---|---|---|
Pipeline, the dataset and the drift.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Registry, the version and not mixing.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Prod, the rubbish and the monitor.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Rollback, the model and not leaving the one that hallucinates.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The scientist, the rush and not skipping the test.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
That it does not serve rubbish, and not skipping the monitor.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 27% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- model pipelines
- drift
- model registry
- monitors
- model rollback
- datasets
- versions
- not serve rubbish
Sources for this job· O*NET 15-2051.00· ESCO/ISCO 2511