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Data Engineer vs ML Engineer: what energy employers ask for

As of 2 November 2026, Plutarch tracked 256 open Data Engineer postings and 74 open ML Engineer postings at energy employers.

Skills and tools

Share of the postings we could read in full that name each skill: 212 for Data Engineer, 67 for ML Engineer.

— means not among those shown on that role's own page, not that no posting names it.

Skills named in Data Engineer and ML Engineer postings.
Skill or toolData EngineerML Engineer
Machine learning—88%
Python77%85%
SQL74%36%
PyTorch—57%
MLOps—48%
LLMs—46%
AWS33%43%
Azure43%36%
CI/CD42%30%
Data warehouse or lakehouse42%—
ETL / ELT pipelines39%—
Databricks35%—
Git33%—
Spark33%—
Statistics—31%
Deep learning—30%
DevOps30%—
Docker—28%
TensorFlow—28%
AI agents—27%
scikit-learn—27%
Power BI23%—
Snowflake23%—
Agile / Scrum19%—
Airflow18%—

Where the postings are

— means not among those shown on that role's own page, not that no posting names it.

Countries of open Data Engineer and ML Engineer postings, as a share of each role's postings.
CountryData EngineerML Engineer
United States39%43%
India11%12%
United Kingdom8%8%
China—5%
Australia—4%
Germany4%—
Poland3%—

How many postings state pay

  • Data Engineer: 54 of 151 postings whose pay we could read (36%)
  • ML Engineer: 23 of 50 postings whose pay we could read (46%)

Pay levels are not compared here: each role's figures come from different countries and currencies. They are on each role's own page. Pay disclosure tracks local pay-transparency law more closely than employer policy, so the two rates partly reflect where each role is advertised.

This compares what employers ask for in the two roles; it does not count how many people hold those skills or say that one role can do the other.

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