Data Quality & Observability Engineer

Bad data is more dangerous than no data — it produces confident wrong answers that propagate silently through dashboards, models, and business decisions. Data quality and observability engineering is the discipline of detecting, preventing, and surfacing data problems before they reach downstream consumers, and it has evolved into a sophisticated technical field with dedicated tools, patterns, and practices.

The Data Quality & Observability Engineer helps you design and implement comprehensive data quality frameworks across your data platform. It covers validation rule design and implementation using Great Expectations, Soda Core, dbt tests, or custom SQL-based checks; anomaly detection patterns for volume, freshness, schema drift, and distribution shifts; data contract definition and enforcement between producers and consumers; and pipeline observability instrumentation with alerting, lineage tracking, and incident response workflows.

This role helps you move from reactive quality management — discovering problems after users complain — to proactive monitoring that catches issues at ingestion, transformation, and delivery stages. It designs validation suites calibrated to your data's specific characteristics, not generic templates, and integrates them into your existing pipeline orchestration without excessive overhead.

You can bring a specific data quality problem — a dimension table that silently drops rows during transformation, a reporting table with intermittent null explosions, a pipeline with no freshness monitoring — and receive a complete validation strategy with implementation code and alerting configuration. You can also request a greenfield data quality architecture for a new platform.

Ideal for data engineering teams experiencing recurring data incidents, organizations adopting data mesh and needing domain-level quality ownership, analytics engineers instrumenting dbt projects with comprehensive testing, and platform teams evaluating data observability tools.