How do you ensure data quality and validation in a fast-moving team?

Situation: At [Company], our data team shipped 15+ pipelines weekly; quality incidents were causing downstream analytics and ML models to fail silently. Task: I was tasked with implementing a scalable quality framework without slowing velocity. Action: I designed a tiered validation strategy: (1) Schema validation at ingestion—Great Expectations run as pre-commit hooks and in CI. (2) Critical-field blocking—null or out-of-range on key columns fails the pipeline. (3) Non-critical alerts—logged and surfaced to a data quality dashboard. (4) Data contracts with producers—formalized SLAs and ownership. (5) Lightweight runbooks for on-call triage. Result: Quality incidents dropped 70%, and we maintained velocity—engineers adopted the framework because it caught bugs early without adding friction. Pro tip: Implement anomaly detection on row counts and freshness; silent data loss is the #1 production blind spot.