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About this interview
A technical interview on Data Quality, pitched at the medium level. A voice AI interviewer leads the conversation, adapts its questions to your answers, keeps you on topic, and afterward gives you honest, specific feedback on where you were strong and where to improve. Expect roughly 30 minutes.
What you'll be assessed on
Define the core dimensions of data quality (completeness, accuracy, consistency, timeliness, uniqueness) and how to measure them
Explain the difference between schema validation, constraint checks, and statistical anomaly detection
Describe how to implement data quality checks in a pipeline without blocking production loads
Articulate what data observability means and how lineage, freshness, and volume monitoring complement testing
Topics covered
Data quality dimensionsCompletenessAccuracy vs consistencyTimelinessUniquenessValidation typesSchema validationConstraint checksStatistical anomaly detectionNon-blocking quality checksQuarantine zone patternQuality alerting designData observability fundamentalsObservability pillars
A few sample questions
Just examples to set expectations - the real interview has many more and adapts to your responses.
“When we talk about the dimensions of data quality, which core ones do you think about, and why does it matter to name them separately instead of just saying 'the data is bad'?
“How would you design a data quality alerting system so that on-call engineers get actionable alerts without being flooded by noise from transient upstream issues?
“What is a data quality SLA, and how would you go about defining one for a core business metrics table in a company that has never had formal quality agreements before?