Data Data Quality

Purpose

Build a data quality framework covering quality dimensions (completeness, accuracy, timeliness, consistency, uniqueness, integrity), automated validation tests (Great Expectations expectations suites, data docs, checkpoints; dbt singular, generic, freshness tests), data observability (Soda, Monte Carlo, Elementary), data SLAs with escalation paths, and data contracts.

Agent Protocol

Trigger

Exact user phrases: "data quality", "data validation", "data profiling", "Great Expectations", "dbt tests", "data observability", "data contract", "schema validation", "data quality check", "data testing", "data monitoring", "quality dimensions", "data freshness", "data completeness", "Soda", "Monte Carlo", "data SLA", "data integrity".