Engineering · Interview Prep
Data Engineer Interview Questions
Data engineering interviews center on SQL depth, pipeline design, and data-model reasoning. Expect a window-function SQL round, a Spark optimization question, a pipeline design scenario, and probing on data quality and cost. This guide covers what modern data teams actually probe.
Try AI Interview PrepTypical loop
3–5 weeks from first contact to offer
Difficulty
High
Question count
13+
Typical interview loop
Data engineering loops almost always include a live SQL round where you write window functions, debug a slow query, or design a schema. Mid-level roles add one pipeline design round; senior roles include two (a batch pipeline and a streaming or lakehouse scenario). Data quality, cost optimization, and stakeholder collaboration are probed at all levels.
- 1Recruiter screen (30 min)
- 2Technical phone screen (60 min SQL + Python)
- 3Onsite: advanced SQL (window functions, CTEs, optimization)
- 4Onsite: data pipeline / ETL system design
- 5Onsite: data modeling round (Kimball vs. activity schema)
- 6Onsite: behavioral with hiring manager
13 real data engineer interview questions
How to approach this
Textbook window function problem. Use ROW_NUMBER() or DENSE_RANK() partitioned by department, ordered by salary DESC, then filter to rank = 2. Clarify: ties treated as same rank (DENSE_RANK) or as distinct (ROW_NUMBER)? That clarification is the signal interviewers look for. Don't use a correlated subquery — much slower and harder to read.
Common mistakes
- Jumping to SELECT MAX without asking about ties
- Writing a correlated subquery when a window function is cleaner and faster
- Missing the partition — producing a global 2nd-highest instead of per-department
Likely follow-ups
- What if two employees tie for second — how should that be returned?
- How would you find the 2nd highest for the top 5 departments by headcount?
General interview tips
- ·When given an SQL question, state your assumptions about nulls, ties, and duplicates before writing. Interviewers count that as a signal.
- ·For pipeline design questions, always include observability — freshness SLA, quality checks, and lineage. Platforms without observability are not production platforms.
- ·Know the modern stack by name: Airflow/Dagster, Spark, Kafka/Flink, dbt, Snowflake/BigQuery/Databricks, Iceberg/Delta, Feast. Interviewers map candidate depth to which tools you can discuss beyond the docs page.
- ·Quantify data work: rows processed, latency, SLA, cost. 'Processed data' is a dead phrase; 'processed 2TB daily with 99.5% freshness SLA at $4K/month' is what senior DEs say.
- ·For behavioral rounds, always name the downstream consumer. DE impact comes through analysts, scientists, and product features — not through pipelines existing.
FAQ
How important is SQL proficiency for a data engineer interview?
Critical. Nearly every onsite has a live SQL round with window functions, CTEs, and query tuning. Practice: advanced joins, window function nuances (RANGE vs. ROWS, ignore nulls), recursive CTEs, pivot/unpivot, and reading execution plans. SQL is the single highest-weighted technical skill in DE interviews.
Do I need to know Spark internals or is using Spark enough?
Knowing how to read the Spark UI, diagnose shuffles, and explain catalyst optimizer at a conceptual level is expected. You don't need to contribute to Spark source, but you should be able to explain why a specific query caused a 200GB shuffle and how you'd fix it. For senior roles, partition strategy and AQE knowledge are assumed.
Are streaming questions common or are most DE roles still batch?
Both, but streaming is increasingly expected. Even batch-first companies now have Kafka or Kinesis somewhere. For mid-level and up, prepare event-driven architecture concepts: watermarks, late-arriving data, exactly-once semantics, and backfills. If the role explicitly mentions Flink or Kafka Streams, go deeper.
How should I prepare for the pipeline system design round?
Practice 3 scenarios: (1) batch ETL from OLTP to warehouse; (2) event streaming with real-time aggregation; (3) ML feature pipeline with offline + online stores. For each, be able to discuss ingestion, transformation, storage format, orchestration, observability, cost, and failure modes. Bonus: prepare a story about backfilling a schema change.
Related Data Engineer Resources
Related role interview guides
Ready for your Data Engineer interview?
Rolevanta generates role-specific interview questions tailored to the exact job description you're preparing for — with answer frameworks you can practice against.
Start Interview Prep Free