data-quality-frameworks

# 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.md` for detailed frameworks, templates, and examples. ## Limitations - Use this skill only when the task clearly matches the scope described above. - Do not treat the output as a substitute for environment-specific validation, testing, or expert review. - Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.