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name, description, risk, source, date_added
| name | description | risk | source | date_added |
|---|---|---|---|---|
| data-quality-frameworks | Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or establishing data contracts. | unknown | community | 2026-02-27 |
Data Quality Frameworks
Production patterns for implementing data quality with Great Expectations, dbt tests, and data contracts to ensure reliable data pipelines.
Use this skill when
- Implementing data quality checks in pipelines
- Setting up Great Expectations validation
- Building comprehensive dbt test suites
- Establishing data contracts between teams
- Monitoring data quality metrics
- Automating data validation in CI/CD
Do not use this skill when
- The data sources are undefined or unavailable
- You cannot modify validation rules or schemas
- The task is unrelated to data quality or contracts
Instructions
- Identify critical datasets and quality dimensions.
- Define expectations/tests and contract rules.
- Automate validation in CI/CD and schedule checks.
- Set alerting, ownership, and remediation steps.
- If detailed patterns are required, open
resources/implementation-playbook.md.
Safety
- Avoid blocking critical pipelines without a fallback plan.
- Handle sensitive data securely in validation outputs.
Resources
resources/implementation-playbook.mdfor detailed frameworks, templates, and examples.