data-quality
Data Quality
Use this skill when the user needs trustworthy datasets, not just successful pipeline runs.
Default stance
- Prevent bad data early when possible
- Validate transformed outputs before publishing them broadly
- Make metric definitions reviewable and owned
- Combine warehouse constraints with pipeline-level tests
Working approach
- Identify what failure would break user trust: missing rows, wrong metric values, schema drift, stale data, or invalid business logic.
- Decide whether the rule belongs in:
- source/ingestion validation
- storage constraints
- transformation tests
- governance and approval workflow
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