Data Engineer interview questions for recruiters

Technical screening questions

Ask for one example per question and listen for specifics.

• What is the difference between ETL and ELT? A good answer: Where transformation happens and why modern warehouses favor ELT.
• How do you handle late-arriving data? A good answer: Reprocessing windows, watermarks and idempotent loads.
• How do you tune a slow Spark job? A good answer: Partitioning, skew, shuffles and caching.
• How do you ensure data quality? A good answer: Tests, freshness checks and alerting.
• How do you design incremental loads? A good answer: Change tracking, merge logic and backfill strategy.

Quick screening questions

Use these when you have only a few minutes.

• Describe the largest pipeline you built and its volume.
• How do you handle late-arriving or bad data?
• Batch or streaming: when did you choose each?

What separates the levels

Match the level to the requirement before you submit.

• Junior: maintains jobs and writes transforms.
• Mid: builds pipelines and models.
• Senior: designs the platform and data contracts.

Red flags

Any of these is a reason to probe further.

• Only wrote SQL reports.
• No orchestration experience.
• Cannot describe a pipeline failure they fixed.

Tools you should hear mentioned

A data engineer with real experience usually names several of these without prompting.

• Spark
• Airflow
• SQL
• Python or Scala
• A warehouse (Snowflake, BigQuery, Redshift)
• dbt

Run the call well

Ask for the last project first, then dig into the second or third question. Take notes on versions, team size and outcomes, and record them with the submission so the vendor’s later technical interview does not surprise you.

Frequently asked questions