Data Quality Engineer (Azure & Databricks)

We're hiring a Data Quality Engineer to design, build and operate automated data quality controls across our enterprise data platform. You'll make sure pipelines, data products and analytical assets are accurate, complete, timely and fit for business use — working with data engineers, stewards, analysts and architects to turn quality expectations into measurable, automated controls.

What you'll do

• Design and maintain automated data quality checks across ingestion, transformation and reporting workflows on Azure and Databricks

• Build reusable validation components, rule templates, data profiling routines and monitoring patterns that scale across domains and pipelines

• Translate business quality expectations into measurable rules, thresholds and acceptance criteria with stewards, owners and analysts

• Monitor quality alerts, investigate failures, run root-cause analysis and coordinate remediation with engineering and source-system teams

• Integrate quality checks into CI/CD, orchestration and production monitoring to catch data defects early

• Document rules, test results, exceptions and control coverage in cataloging, data observability and governance tools

• Support anomaly detection, reconciliation and regression testing for new sources, platform changes and strategic initiatives

• Partner with the Data Architect, Data Engineering Manager and Data Governance & Business Analyst Manager to align quality standards with architecture, governance and stewardship

What you'll bring

• 4+ years in data engineering, data quality engineering, data testing, analytics engineering or data platform delivery

• Strong SQL and Python for validation, profiling, reconciliation, anomaly detection and automated testing

• Hands-on with cloud data platforms — Azure Data Factory, Azure Data Lake, Databricks, Spark, Delta Lake or equivalents

• Experience implementing data quality or data observability frameworks — Great Expectations, dbt tests, Soda, Databricks Expectations, Deequ, Monte Carlo or custom-built

• Working understanding of data governance — ownership, stewardship, metadata, data lineage, classification and quality issue management

• Excellent communication and collaboration in English, with technical and non-technical stakeholders alike

• Nice to have: Microsoft Purview, Unity Catalog, Azure Monitor, Log Analytics, Power BI or Microsoft Fabric; CI/CD, version control, Infrastructure as Code and automated test frameworks, in Agile or hybrid delivery