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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
<!-- Source: .faos/custom/skills/data/data-quality-frameworks/SKILL.md -->
