Design a data quality framework for a modern data platform.

What's really being asked

This question tests your ability to think architecturally about data quality as a continuous, multi-layered process. The interviewer is evaluating whether you can move beyond isolated technical fixes (like writing a single validation test) to designing a comprehensive framework that integrates people, processes, and technology. They are looking for a strategic mindset that connects data quality initiatives directly to business value, like reducing the 40% of engineering time spent firefighting data errors.

The full answer

An excellent answer outlines a four-part framework in logical order. First, establish a Data Governance structure by defining roles and responsibilities. This includes a governance committee for strategy, Data Stewards (from the business) who own specific datasets, and Data Custodians (engineers) who implement the technical controls. Mentioning a RACI matrix shows senior-level thinking. Second, describe Data Profiling and Assessment. This is the discovery phase where you measure the current state by running column statistics (null counts, cardinality, min/max) and benchmarking datasets against dimensions like accuracy, completeness, and timeliness. Third, define Standards, Rules, and Metrics. Explain how you translate business logic into machine-readable rules (e.g., Order_Date must be before Ship_Date) and create metrics (e.g., duplicate rate) that roll up into executive scorecards. Crucially, these metrics must be tied to business KPIs. Fourth, detail the technical implementation within the data pipeline. This includes schema validation at ingestion using a schema registry, data cleansing and standardization steps, anomaly detection, and lineage tracking to ensure traceability.

The mistakes people make

A tool-first answer is a major red flag. Saying "I'd use Great Expectations and dbt tests" without first establishing the governance and business context shows a junior, tactical approach. Another common mistake is being vague about governance. Simply saying "we need good governance" is not enough; a senior candidate will specify the roles of stewards and custodians and how they interact. Finally, proposing technical metrics without linking them to business impact (e.g., tracking nulls without explaining how that affects revenue or operations) misses the entire point of the exercise.

What usually comes next

Expect questions like: "How would you prioritize which data quality issues to fix first?" to test your ability to assess business impact. Or, "How would you get buy-in from business stakeholders to fund this framework?" to test your communication skills. Another common one is, "An upstream source changes its schema without warning; how does your framework handle this?" testing your plans for schema drift and incident response.

A concrete example

Gartner estimates poor data quality costs companies 10-20% of revenue. For an e-commerce company, a 5% duplicate customer record rate leads to wasted marketing spend and poor customer service. My framework would first assign a Data Steward from Sales to own the customer dataset. We'd profile the data to confirm the 5% duplicate rate and set a KPI to reduce it to <1%. The Data Custodians (engineers) would then implement a deduplication model in the pipeline and a validation rule to block new potential duplicates. We'd track this metric on a weekly scorecard for the Sales VP to show ROI.