Data Quality Frameworks

<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT -->
---
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


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