Data Engineer

01What the role is

If the data scientist frames the problem and the ML engineer serves the model, the data engineer is the one who makes sure there is trustworthy data to work with in the first place. They build the pipelines, warehouses, and ETL processes so that when anyone downstream needs a dataset, it is clean, current, and reliable. It is unglamorous and absolutely load-bearing: nothing else in the AI stack works without it.

02What they do day to day

Python and SQL are table stakes, in roughly 70 percent of postings.⁴ The skills that actually move an offer from $120,000 to $160,000 and up are the modern data stack: Apache Spark, Snowflake and Databricks, Kafka and real-time streaming, and cloud platforms, plus growing demand for data governance and MLOps-adjacent work as pipelines start feeding models.⁴

• Designing and maintaining batch and streaming data pipelines.
• Building and tuning warehouses and lakehouses on Snowflake, Databricks, or cloud-native stacks.
• Ensuring data quality, lineage, and governance.
• Increasingly, feeding and supporting ML workflows.

03What it pays

Average base pay sits around $130,000 to $137,000 across the major sources, with a wide spread by level.¹² Senior data engineers run $160,000 to $215,000 base, and total compensation reaches $190,000 to $300,000 at strong tech employers once equity stacks on. At the largest employers, senior total comp runs roughly $300,000 to $420,000, with staff and above clearing $500,000 on equity.³

The base band is narrow and most sources agree on it. The $50,000 or so of premium riding on top, driven by specialization, metro, and company stage, is what decides whether an offer closes.³

04Why this role matters to data centers

AI runs on data, and data engineers build the plumbing that gets it to the models. Every training run scheduled on a data center's GPUs depends on pipelines a data engineer built and maintains. As AI adoption accelerates, engineers who can support ML workflows and keep governance intact are increasingly the bottleneck between a company's data and the compute it has paid for.

05How to hire one

Do not post a data scientist req when the work is pipeline building, and do not post a data engineer req at a data-analyst salary. Both are expensive ways to refill the seat in nine months. Price to the senior band if the work is senior, and reach the specialists, the Spark, streaming, and lakehouse people, who are employed and not scrolling boards.