The Data Engineer is the engineer who builds and maintains a company's data infrastructure: they collect data from applications, tools and sensors, transform it, store it and make it available, reliable and up to date, for analytics and AI.
In practice, they design pipelines that run daily or in real time, orchestrated by tools such as Airflow, transformed with dbt or Spark, and stored in a warehouse or lakehouse (Snowflake, BigQuery, Databricks). They monitor data quality, manage compute costs and access security.
Their work sits upstream of the Data Analyst's and the Data Scientist's: without solid pipelines, their analyses and models rest on wrong or incomplete data. They are close to back-end developers in their practices (code, tests, CI/CD) and to infrastructure engineers in their cloud tooling.
Why it matters when hiring
It is often the most cost-effective data hire, and the most underestimated. The right profile has a real software engineering culture: versioned code, tests, reviews, monitoring. In an interview, ask how they detect corrupted data before it reaches a dashboard. The trap: an analyst who has written a few SQL scripts and presents themselves as a Data Engineer.