Data Engineering
Data Quality & Observability
Catch bad data before it reaches a dashboard.
The Problem
Data teams too often find out about broken pipelines from the business: a wrong number in a dashboard, a failed model, an angry stakeholder. By then the damage — and the trust erosion — is done.
Without lineage, every incident becomes a manual hunt across dozens of tables and jobs to find where the data went wrong.
What it does
Automated tests
Freshness, volume, schema, and business-rule checks run on every pipeline, so regressions fail loudly and early.
Anomaly detection
Monitors learn normal patterns and flag drift, spikes, and nulls without hand-tuned thresholds.
End-to-end lineage
Column-level lineage maps every metric back to its sources, turning multi-hour incident hunts into minutes.
Actionable alerting
Routed, deduplicated alerts to Slack or PagerDuty with the context to triage — not noise.
How it works
We instrument your existing warehouse and pipelines — no rip-and-replace.
Quality checks and anomaly monitors are defined as code and version-controlled alongside your models.
Incidents surface with lineage and owner context, so the right person fixes the root cause fast.
Tech stack
Capabilities and typical outcome ranges reflect our delivery patterns and published industry benchmarks; actual results depend on your data and environment.