Implements comprehensive data validation using Great Expectations, dbt tests, and data contracts to ensure reliable data pipelines.
The Data Quality Frameworks skill provides a standardized approach to maintaining high-integrity data environments. It offers production-grade implementation patterns for industry-standard tools like Great Expectations and dbt, alongside modern data contract specifications. By focusing on critical dimensions such as completeness, uniqueness, and timeliness, this skill enables data engineers to build automated testing pyramids that catch schema drift and logic errors early in the development lifecycle.
Key Features
01Multi-dimensional data quality metric tracking
02Automated validation in CI/CD pipelines
03Great Expectations suite and checkpoint orchestration
04Data contract definition for cross-team alignment
050 GitHub stars
06Generic and singular dbt testing patterns
Use Cases
01Enforcing schema and business rules in production data warehouses
02Preventing breaking changes with data contracts between services
03Automating data monitoring and alerting via Slack or email