Data Engineering Interview Prep (2026)

A data engineering interview is a structured loop that tests whether a candidate can build, operate, and reason about production data systems. Unlike software engineering loops, which lean on data structures and algorithms, data engineering loops are organized around five domains: SQL, Python, data modeling, system design for pipelines, and behavioral. The same five domains show up at every level of seniority. Only the depth, scope, and judgment expectations change.

The 2026 data engineering interview loop typically runs 5 to 7 rounds. The first is a recruiter screen (30 minutes, role and comp expectations). The second is a technical screen, usually live SQL or Python coding (45 to 60 minutes). Many companies follow with a take-home assignment, ranging from a 90-minute SQL exercise to a multi-day pipeline build. The onsite, virtual or in-person, is the 4-to-5-round main event: two coding rounds (SQL plus Python or PySpark), one data modeling round, one pipeline system design round, and one behavioral round.

The SQL round tests fluency under time pressure: window functions (ROW_NUMBER, RANK, LAG, LEAD, frame clauses), complex joins, conditional aggregation, CTEs and recursive CTEs, NULL handling, and the ability to translate a vague business question into a working query in one pass. Most rejections at this round are not from getting the answer wrong; they are from taking too long to get there. Practice for speed, not novelty.

The Python round tests data wrangling and ETL logic. Expect pandas operations (groupby, merge, transform, pivot), file parsing (CSV, JSON, gzipped logs), dictionary and list comprehensions, basic class design, and increasingly often a PySpark variant. The bar is not whether you can write Python; it is whether you can write the kind of Python a data engineer writes on the job, which is closer to a Jupyter notebook than to a LeetCode solution.

The data modeling round is where most loops are decided. You will be given a product description (a ride-share app, a streaming service, an e-commerce site) and asked to design the warehouse schema. Strong answers cover fact and dimension grain, slowly changing dimensions (Type 1, 2, and 6 are the ones that come up), surrogate keys, and the tradeoffs between star, snowflake, and data vault approaches. Weak answers either skip grain entirely or over-normalize.

The system design round for data engineering looks different from the SWE version. You will design a pipeline, not a service. Common prompts: build a near-real-time fraud detection pipeline, a daily revenue reporting pipeline, a user-event aggregation pipeline. Strong answers explicitly choose between batch and streaming, name the orchestration tool, address late-arriving data, plan backfill strategy, and call out failure modes (partial writes, dedup, schema drift).

The behavioral round is graded on STAR-format storytelling: situation, task, action, result. Senior loops add scope and ambiguity dimensions, with prompts like tell me about a time you made a tradeoff under uncertainty or tell me about a time you owned an outcome across teams. Most rejections are not from missing examples; they are from rambling, burying the result, or failing to name what you specifically did versus what the team did.

Companies vary in emphasis. Meta and Amazon lean SQL-and-modeling-heavy. Stripe and Databricks push system design depth. Netflix and Airbnb bias toward streaming and large-scale event processing. The role level matters as much as the company: an L5 staff loop at any of them will test scope, tradeoffs, and decision documentation in ways an L4 mid-level loop will not. Reference the U.S. Bureau of Labor Statistics data engineer occupation page for level definitions and typical compensation ranges.

The fastest way to prep is to practice with real execution: SQL queries that run against a real database, Python that executes against real input, schemas you can validate. Reading solutions builds recognition; running code under a timer builds the recall speed every round demands. Each section below is a deep-dive into one slice of the loop, with practice problems linked at the end.