Data Quality Auditor

End-to-end data quality (DQ) practice: define DQ dimensions, write rule-based checks, detect schema drift, monitor freshness SLAs, respond to DQ incidents, build a maturity-graded program. Tool-agnostic — works whether you use Great Expectations, dbt tests, Soda Core, Monte Carlo, custom SQL, or hand-rolled scripts.

This skill is audit-focused, not pipeline-focused. For pipeline design, ETL, Spark/dbt, see engineering/senior-data-engineer.

Core Capabilities

• Six DQ dimensions — completeness, accuracy, consistency, timeliness/freshness, validity, uniqueness (plus integrity, conformity, reasonableness); at least one check per dimension at production stage.
• DQ check catalog — five categories (volume, freshness, schema, values, distribution) of ~50 specific check patterns applied per dataset.
• Schema drift detection — snapshot baseline schemas and diff added/removed/changed columns, types, and ordinals with severity.
• Freshness SLA monitoring — per-table max-age budgets with alerting-ready output.
• Incident response — severity classification (Sev1-4) and a 7-step playbook (acknowledge → quarantine → triage → contain → fix-forward → notify → post-incident) with recovery patterns.
• Maturity & governance — a five-level maturity model and anti-pattern catalog to grade and improve a DQ program.

When to Use