How do you enforce data quality in a data pipeline?
Assesses fundamental understanding of Data Engineering conventions, runtime behavior, and memory/performance considerations.
Hiring managers look for precision, avoidance of ambiguous jargon, and ability to explain trade-offs under real production conditions.
Data quality checks should run as first-class pipeline steps, failing or quarantining bad data rather than silently loading it.
Dimensions to test:
- Completeness: no unexpected nulls in required columns.
- Uniqueness: primary keys are unique.
- Validity: values fall in allowed ranges or formats, such as ISO dates.
- Consistency: totals reconcile across tables.
- Freshness: the table was updated within the expected window.
- Referential integrity: foreign keys exist in the dimension.
Implement with tools like Great Expectations, dbt tests, Soda or custom assertions. Add a circuit breaker that halts downstream jobs on critical failures, and route failed rows to a quarantine table for inspection. Publish metrics and alerts so issues are caught before they reach dashboards. Record expectations as code and review them like application tests.
Candidate Response Strategy & Interview Tips
- Start with a concise one-sentence summary: Deliver a direct, confident answer first before expanding into nuances.
- Demonstrate real-world trade-offs: Discuss where this approach excels and when you would avoid it in production systems.
- Discuss complexity & edge cases: Proactively explain time/space complexity or boundary conditions (null values, scale limits).
- Prepare for interviewer follow-ups: Technical hiring panels frequently probe deeper into concurrency, backward compatibility, or alternative libraries.