Data Quality Debugging and Root Cause Analysis Questions

Focuses on investigative approaches and operational practices used when data or metrics are incorrect. Includes techniques for triage and root cause analysis such as comparing to historical baselines, segmenting data by dimensions, validating upstream sources and joins, replaying pipeline stages, checking pipeline timing and delays, and isolating schema change impacts. Candidates should discuss systematic debugging workflows, test and verification strategies, how to reproduce issues, how to build hypotheses and tests, and how to prioritize fixes and communication when incidents affect downstream consumers.

HardTechnical

33 practiced

Case study: Model performance dropped following a pipeline change; label distributions changed because labeling ETL now drops certain classes. Outline how you'd investigate label skew, reproduce the label derivation, and propose immediate and long-term remediation including monitoring to prevent recurrence.

HardTechnical

45 practiced

A Spark job is failing or taking excessively long due to data skew on a join key. Describe how you would detect skew, short-term mitigation techniques (salting, broadcast join, repartition), and long-term fixes in upstream data modeling to prevent skew. Include metrics you would gather.

EasyTechnical

46 practiced

List quick sanity checks you would run when you see a numeric model feature containing unexpected NaNs in production. Include data checks, pipeline checks, and temporary mitigations to keep production models safe.

EasyTechnical

33 practiced

Given a table events(event_id UUID PRIMARY KEY, user_id BIGINT, event_type TEXT, event_ts TIMESTAMP, source TEXT), write a SQL query to compute the daily null rate for user_id over the last 60 days grouped by source, and flag days where null rate > mean + 3*stddev computed over the preceding 28-day window. Explain assumptions about small sample sizes and how you'd avoid false positives.

HardTechnical

34 practiced

Case study: A billing metric was inflated 4x for a 2-hour window and downstream customers were invoiced incorrectly. You have logs, job run metadata, and dataset snapshots. Describe how you would build a timeline, perform root cause analysis, decide whether to backfill, and communicate with business and customers. Include immediate mitigations and long-term fixes.

Unlock Full Question Bank

Get access to hundreds of Data Quality Debugging and Root Cause Analysis interview questions and detailed answers.

Join thousands of developers preparing for their dream job.