Data Quality Engineering: Building Trustworthy Data Systems at Scale

What dependable data quality requires

My approach connects structural validation with semantic checks, reconciliation across independent sources, anomaly detection, lineage, observability, and evidence that can explain why a result should be trusted.

Structural and semantic corruption

Structural failures are visible when fields, types, or required records are missing. Semantic corruption is harder: the record is technically valid, yet its meaning has drifted, its source is stale, or it conflicts with another valid system.

Engineering practices

Validate meaning as well as format.
Reconcile independent evidence instead of assuming a single source is authoritative.
Make data-quality decisions observable and explainable.
Design recovery paths, not only alerts.
Measure the quality of the evidence used during incident response.