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.
This tests whether you can embed governance into pipeline engineering rather than bolting it on later. A strong answer starts with data stewards, CDE ownership, and a RACI matrix. It then layers schema registries and contracts at ingestion, continuous profiling with statistical anomaly detection in CI/CD, column-level lineage for impact analysis, and executive scorecards mapped to business KPIs. Red flag: jumping straight to tools like Great Expectations or Monte Carlo without defining ownership, quarantine workflows, or remediation loops.
Read the original → ewsolutions.com
- #data quality
- #system design
- #data governance
- #pipeline architecture
- #lineage
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