Building Trust In Data: How We Added Data Quality Checks To Our Scoring Data Pipeline

This blog post describes SecurityScorecard’s approach to building repeatable, observable data quality checks across a complex Airflow pipeline using Great Expectations for schema validation, DataHub for centralized observability, and database-side SQL for performance-sensitive business rule checks. The team implemented multi-stage validation (input, output, database, final gate), publishes assertions to DataHub for trending and alerting, and emphasizes using the right tool for each check to improve detection, response speed, and developer confidence.