Data Quality Frameworks

This one's about baking data quality checks into your pipelines using Great Expectations, dbt tests, and data contracts. You'd reach for it when you need to validate data between teams, set up automated quality gates in CI/CD, or monitor whether your data meets the rules you've defined. It covers the practical stuff like defining expectations, setting up alerts, and deciding what breaks the build versus what just warns. The skill includes an implementation playbook with templates, which is helpful since these frameworks can get verbose fast. Just don't use it to block production pipelines without a fallback plan, and remember that validation rules are only as good as the quality dimensions you choose to measure.