Data Quality Frameworks

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name: data-quality-frameworks description: 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. tags: [data, data-engineering]

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.

Safety

• Avoid blocking critical pipelines without a fallback plan.
• Handle sensitive data securely in validation outputs.

Resources