Data Engineering Interview Prep (2026)

What to expect

You receive a schema with 2-5 tables and a business question. You write SQL live, usually in a shared editor or on a whiteboard. 32.7% of all interview rounds are phone-screen SQL, making it the single most common round format. The interviewer watches your thought process as much as your final query.

What gets tested

GROUP BY (15.3% of SQL questions), INNER JOIN (13.2%), PARTITION BY / window functions (9.7%), LEFT JOIN (7.9%), ROW_NUMBER (6.2%), RANK (4.9%), SUM/AVG (7.8%), and COUNT (6.2%). Senior roles add query optimization discussion.

How to prepare

Practice writing SQL from scratch, not reading solutions. Time yourself: 15 minutes per medium problem, 25 for hard. Focus on window functions and CTEs first. Run every query against real data to catch edge cases.

Common mistakes

Forgetting NULL behavior in JOINs and WHERE clauses. Writing correct logic but unreadable queries. Not talking through your approach before writing. Ignoring edge cases like empty tables or duplicate rows.

What to expect

You write Python to solve a data processing problem. This is NOT algorithm-heavy coding. Expect file parsing, data transformation, dictionary manipulation, and basic ETL logic. 6.0% of rounds are onsite Python specifically. Some companies use a shared IDE; others use a plain text editor.

What gets tested

For loops (13.1% of Python questions), function definitions (9.0%), list manipulation (8.2%), algorithms (7.9%), dictionary operations (7.1%), if/else logic (6.3%), classes (4.4%), and sorting (3.6%). Most interviewers want vanilla Python, not pandas.

How to prepare

Practice without pandas. Most interviews want you to use built-in Python. Write functions that parse nested JSON, deduplicate records, and join two datasets by key. Test with edge cases: empty inputs, missing keys, type mismatches.

Common mistakes

Reaching for pandas when the interviewer wants vanilla Python. Not handling exceptions for malformed input. Writing code that loads everything into memory at once. Forgetting that dict.get() returns None by default.

What to expect

You design a schema for a given business scenario, then defend your choices. 4.7% of rounds are onsite data modeling specifically. The interviewer pushes back on trade-offs: why this grain? Why denormalize here? What happens when requirements change?

What gets tested

Entity identification (6.6% of modeling questions), primary keys (5.9%), attributes (5.9%), foreign keys (4.7%), star schema (4.7%), fact tables (4.7%), dimension tables (4.2%), and medallion architecture (3.6%). Senior roles go deeper on trade-off reasoning.

How to prepare

Practice designing schemas for real scenarios: e-commerce orders, event tracking, user permissions, content management. For each design, write down 3 trade-offs you made and how you would explain them. Practice defending your choices out loud.

Common mistakes

Over-normalizing when the use case is analytical. Not discussing how the schema handles future requirements. Forgetting to define grain for fact tables. Choosing surrogate keys without explaining why natural keys are insufficient.

What to expect

System design appears in only 2.8% of interview rounds overall, but it is concentrated in onsite loops at senior levels (2.6% of rounds are onsite system design). You design a data pipeline end-to-end for a given scenario. The interviewer probes on scale, fault tolerance, data quality, cost, and monitoring. This round is mostly verbal with whiteboard diagrams. There is no coding.

What gets tested

Batch vs streaming trade-offs. Idempotent processing. Schema evolution strategies. Data quality validation. Orchestration and dependency management. Monitoring, alerting, and SLA definition. Cost optimization at scale.

How to prepare

Study 5 canonical pipeline patterns: CDC ingestion, event streaming, daily batch ETL, reverse ETL, and real-time feature serving. For each, know the components, failure modes, and scaling bottlenecks. Practice talking through a design in 20 minutes.

Common mistakes

Jumping to specific tools before establishing requirements. Not discussing failure modes and recovery. Ignoring data quality checks. Designing for scale you do not need. Forgetting monitoring and alerting entirely.

What to expect

Behavioral rounds account for 2.5% of the overall interview process, but they carry outsized weight in the final hiring decision. Expect questions about debugging production pipelines, handling data quality incidents, working with stakeholders who have conflicting requirements, and prioritizing tech debt vs new features.

What gets tested

Communication clarity. How you handle ambiguity. Incident response instincts. Cross-team collaboration. Ownership and accountability. How you make trade-off decisions under time pressure.

How to prepare

Prepare 5 stories using the STAR format (Situation, Task, Action, Result). Include at least one production incident, one cross-team project, and one time you had to push back on a requirement. Quantify results where possible: latency reduced by X%, data freshness improved from hours to minutes.

Common mistakes

Giving vague answers without specific details. Taking credit for team work without acknowledging the team. Not having a production incident story ready. Failing to explain the business impact of your technical decisions.