ETL Testing
before bad data makes good teams wrong

High-risk source feeds, schema contracts, and dependency chains behind revenue, billing, reporting, and decision-critical metrics.

Each drift path by business blast radius, drift frequency, and detection lag so fixes happen in the right order.

A fix-first drift map that replaces dashboard surprises with actionable pipeline priorities.

Transformation logic across edge conditions, late-arriving records, null handling, deduplication, and temporal window behavior.

Historical replay and mutation scenarios that expose logic brittleness before bad data reaches downstream teams.

Confidence that transformed outputs remain decision-safe even as sources, rules, and workloads evolve.

Row-level and aggregate reconciliation, control totals, freshness thresholds, and business-rule assertions between source and destination layers.

Where drift, truncation, missing joins, timezone shifts, or transform defects leak misleading numbers into dashboards and workflows.

Evidence-backed quality gates that stop untrusted data from reaching business decisions.

Data validation suites and release criteria directly in your CI/CD and orchestration flows, linked to drift and reconciliation thresholds.

Operating standards so data and engineering teams can run gates continuously without outside dependency.

A client-owned data confidence system that compounds with each release instead of resetting after incidents.