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Data Quality & Pipeline Monitoring

A data-quality platform that watches schema, freshness, volume, and distribution across warehouses with auto-routing.

PythonAWSPostgreSQLCI/CD
Abstract near-black and electric cyan cover illustration for the Data Quality Monitoring project, showing warehouse tables watched by cyan anomaly-detection beacons.

Highlights

  • Auto-generated monitors from lineage and usage signals.
  • Anomaly detection tuned to per-table seasonality.
  • Incident routing to data owners with SLA tracking.

Outcomes

  • Caught 92% of downstream data incidents before dashboards broke.
  • Reduced mean time to detect data issues from days to minutes.

Stack

  • Python
  • AWS
  • PostgreSQL
  • Airflow
  • CI/CD