Data Quality & Observability
Data quality work is what makes the rest of the platform trustworthy. Tests at ingestion and at each transformation, monitoring on pipeline health, and alerting that reaches whoever can act on it, so data breaks loudly rather than silently.
We implement quality checks where data enters the pipeline and again at each major transformation, not only as a final validation step, so problems get caught close to their source where they are cheapest to fix.
Capabilities
Data Quality Testing
dbt tests, Great Expectations, or custom rules for your failure modes.
Observability Dashboards
Monitoring on pipeline health, freshness and volume.
Alerting
Alerts routed to the team that can act, with context to start debugging.
Incident Runbooks
Written procedures for the failure modes found during implementation.
How It Comes Together
A typical data platform architecture
Source systems and events land reliably in a raw layer.
dbt models turn raw data into trusted, tested metrics.
Governed storage optimized for your query patterns.
Dashboards and reports surface insight for decision-makers.
Reverse ETL syncs insight back into the tools your team uses.
In Practice
Data pipelines that fail silently are more dangerous than pipelines that fail loudly, because a loud failure gets fixed and a silent one erodes trust in every downstream report for weeks before anyone notices. We implement quality checks at the point data enters your pipeline and again at each major transformation, not just as a final validation step, so problems get caught close to their source where they're cheapest to diagnose and fix. Tests are written for the failure modes that actually matter to your business, a schema change from a source system, a sudden drop in row count, a metric drifting outside historical bounds, rather than generic checks that generate noise without catching real issues. Every observability implementation includes alerting routed to the team that can actually act on it, with enough context in the alert to start debugging immediately, and a runbook for the failure modes we've identified during implementation. The goal is a system where data quality issues get caught and fixed before a stakeholder ever notices something looks wrong in a report.
Related Services
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Common questions
The ones matched to how your data really fails. A schema change from a source system, a sudden drop in row count, a metric drifting outside its historical range. Generic checks applied everywhere generate noise, and a team that has learned to ignore the alerts is worse off than one with no alerts at all.
At ingestion and again at each major transformation, not only as a final validation step before the dashboard. Catching a problem close to its source is the difference between fixing one model and unpicking which of forty downstream tables inherited the bad value.
Quality testing asserts that specific things are true about the data. Observability is knowing the state of the pipeline itself: freshness, volume, run duration, and whether anything failed. You need both. A pipeline that ran successfully on stale input passes every quality test and is still wrong.