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Design a data quality framework from source to consumption
This tests full-lifecycle data architecture. Strong answers define ownership first, then schema contracts at ingestion, profiling and anomaly detection in CI/CD, column-level lineage, and KPI-linked scorecards. Red flag: tools before ownership or RACI.
Design a framework for ensuring data quality and integrity
This tests your ability to design a proactive, multi-layered data quality system, not just reactive fixes. Start with governance (roles/ownership), then detail profiling, validation, and cleansing. Finally, discuss lineage. Red flag: focusing only on one tool.
Design a data quality framework for a modern data platform.
Tests your ability to design a systematic data quality strategy. A great answer outlines a framework starting with governance (roles), then profiling/assessment, defining standards, and finally implementing pipeline controls.
How do you handle 10% nulls in a key numerical column?
Check MCAR/MAR/MNAR; prefer median or mean imputation; add an "is_missing" indicator.
How would you handle 10% null values in a key column?
This tests your understanding of data cleaning trade-offs. First, investigate the cause of nulls. Then, discuss simple imputation (mean/median) vs. discarding rows, weighing pros and cons. A red flag is jumping to a solution without asking about the data.
How would you handle 10% nulls in a key column?
This tests your ability to diagnose data issues before solving. First, investigate why data is null. Then, discuss trade-offs of dropping vs. imputing with the mean or median, considering the impact on the dashboard's accuracy.
What validation checks would you implement for a user-submitted email field?
Tests whether you separate syntax validation from deliverability and know practical ingestion guards. A strong answer covers RFC-aware syntax, domain checks, normalization, and deduplication.
What validation checks would you implement for an email field?
Tests your understanding of practical validation vs. theoretical purity. A great answer prioritizes user experience, uses simple syntax checks (like a single '@'), and relies on sending a verification email as the ultimate test.
How would you validate user-submitted email addresses at ingestion?
Tests your understanding of data validation beyond simple regex, focusing on robustness and system-level thinking. A good answer covers format checks, DNS/MX record validation, and blocking disposable services.
What is a data schema and why enforce it at ingestion?
Tests schemas as contracts and ingestion validation as a quality gate. Strong answers cite blueprints with constraints, fail-fast ingestion catching type errors upstream, and downstream trust. Red flag: treating schemas as optional docs affecting only storage.
What is a data schema and why enforce it on ingestion?
This tests your understanding of data contracts. A great answer defines a schema as a data blueprint, then explains how early enforcement prevents bad data, ensuring consistency and reliability for analytics. A red flag is only defining the term.
What is a data schema and why enforce it on ingestion?
This tests your grasp of data governance. Define a schema as a data blueprint. Explain that enforcement on ingestion prevents "garbage in, garbage out" by validating types and formats, ensuring data is usable for analytics.
How would you implement data lineage for microservices analytics?
Tests end-to-end provenance tracking, not just docs. Strong answers cover automated metadata capture at service boundaries, a central catalog such as DataHub or OpenLineage, and column-level tracing.
Explain data lineage and how you would implement it
This tests your ability to design for data observability. Define lineage (origin, transformation, movement), then propose a solution using metadata extraction (OpenLineage) and a central graph store/UI (Marquez) to trace data from microservices to analytics.
Explain data lineage and how you'd implement it
Tests your practical data governance knowledge. Define lineage (origin, transforms, movement), then outline a solution: metadata collection from services, a central store (graph DB), and a visualization tool (Amundsen/OpenLineage).
Design a CDC pipeline that handles schema evolution gracefully
Tests designing resilient CDC pipelines against schema drift. A strong answer covers schema registries with versioning, backward-compatible serialization, and automated compatibility checks.
Handle Schema Evolution in a CDC Analytics Pipeline
This tests your ability to design robust data systems that anticipate change. A great answer includes a schema registry, compatibility rules, a dead-letter queue for non-compliant records, and automated alerting.
How to handle schema evolution in a CDC analytics pipeline?
This tests your ability to design resilient CDC pipelines. A strong answer outlines automated schema detection, using a flexible format like Avro, and enforcing governance with a schema registry. A red flag is proposing manual fixes for every change.
How would you build a Customer golden record across fragmented systems?
This tests master data management discipline for distributed, conflicting records. A strong response covers identity resolution, survivorship rules, merge architecture, lineage, and feedback loops.
How do you create a 'golden record' from fragmented data?
Tests your ability to design a data reconciliation system. A great answer outlines a process: profiling sources, defining survivorship rules, implementing a matching engine, and creating a stewardship process. A red flag is suggesting a simple one-off script.