data-quality-frameworks

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.
risk	unknown
source	community
date_added	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.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.