How do you manage data quality?

Data quality management starts with defining quality dimensions—accuracy, completeness, consistency, timeliness, and validity. I implement a layered approach: 1) Data profiling during ingestion to detect anomalies; 2) Validation rules in ETL jobs (e.g., schema checks, range checks, uniqueness constraints); 3) Automated data quality dashboards using tools like Great Expectations or AWS Glue DataBrew; 4) Alerting via CloudWatch or PagerDuty when thresholds are breached; 5) Data stewardship processes for root‑cause analysis and remediation. I also maintain lineage metadata in a catalog (e.g., AWS Glue Data Catalog) to trace issues back to source systems.