How to Hire a Data Engineer
Data engineers build the pipelines and infrastructure that make your data usable. Without them, analysts and data scientists are blocked, and business decisions are made on stale or incomplete information. Hiring the right data engineer is foundational to any data-driven organization.
What to Look For
- Strong SQL skills and understanding of query optimization
- Experience building and maintaining ETL/ELT pipelines
- Proficiency with data warehouse platforms (Snowflake, BigQuery, Redshift)
- Understanding of data modeling: star schema, normalization, and slowly changing dimensions
- Workflow orchestration experience (Airflow, Prefect, or similar)
- Ability to work closely with analysts and data scientists to understand data requirements
The Hiring Process
- 1
Technical screen — SQL and data modeling
Give a multi-table SQL problem and a data modeling scenario. These are the clearest signals of foundational ability.
- 2
Pipeline design challenge
Ask them to design a pipeline for a realistic use case (e.g., ingesting e-commerce events into a warehouse for reporting). Evaluate for completeness, reliability, and schema design.
- 3
Data infrastructure interview
Discuss their experience with orchestration, monitoring data quality, and handling schema changes upstream.
- 4
Cross-functional collaboration assessment
Ask how they work with analysts and scientists. Can they translate business requirements into technical designs?
Interview Tips
- Give a SQL query and ask them to optimize it — watch for index awareness, query plan thinking, and avoiding N+1 patterns
- Ask 'How do you handle late-arriving data in a pipeline?' — tests understanding of streaming vs. batch trade-offs
- Probe on data quality: 'What does your monitoring setup look like for a production pipeline?'
- Ask about schema evolution: 'A source system changes a column type — how do you handle this downstream?'
Red Flags
- Weak SQL fundamentals — this is non-negotiable for data engineers
- No experience with pipeline monitoring or data quality checks
- Builds pipelines without documenting lineage or schema
- Never worked with analysts or scientists to understand downstream needs
- Treats all data as equally reliable without questioning source quality
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