IS WORK OVER?

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

Data 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.

TaskShareAIPoints
Design pipelines, models and orchestration.
Estimate

No public source provides a precise figure. This is our estimate.

24%50%+12
Write SQL, Spark and data tests.
Estimate

No public source provides a precise figure. This is our estimate.

18%45%+8.1
Incidents, quality and lineage in production.
Estimate

No public source provides a precise figure. This is our estimate.

16%22%+3.5
Talk to analysts and negotiate data contracts.
Estimate

No public source provides a precise figure. This is our estimate.

16%18%+2.9
Cloud cost, permissions and PII.
Estimate

No public source provides a precise figure. This is our estimate.

14%15%+2.1
Decide architecture and technical debt.
Estimate

No public source provides a precise figure. This is our estimate.

12%20%+2.4
Total100%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

Olive-oil miller

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.

TaskShareAIPoints
Intake, yield and the mill log.
Estimate

No public source provides a precise figure. This is our estimate.

16%40%+6.4
Crush, malaxate and the decanter.
Estimate

No public source provides a precise figure. This is our estimate.

18%12%+2.2
Temperature, time and the fruitiness.
Estimate

No public source provides a precise figure. This is our estimate.

16%28%+4.5
Cellar, the batch and the one that goes rancid.
Estimate

No public source provides a precise figure. This is our estimate.

16%22%+3.5
The campaign, the tractor queue and not heating.
Estimate

No public source provides a precise figure. This is our estimate.

18%18%+3.2
Cold for real, and not painting the yield.
Estimate

No public source provides a precise figure. This is our estimate.

16%12%+1.9
Total100%22%

Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low

Skills and activities

  • crush olives
  • malaxation
  • decanter
  • fruitiness
  • the campaign
  • mill yield
  • oil cellar
  • extraction temperature

Sources for this job· O*NET 51-3092.00· ESCO/ISCO 7514

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