Role Overview :

We are looking for a Fabric Data Quality Engineer. The role will focus on building automated data-quality frameworks, validating migrated data, performing source-to-target reconciliation, and ensuring data and BI outputs meet business requirements.

This is a hands-on engineering role requiring strong SQL, Python/PySpark, Microsoft Fabric/Azure, data validation, and reconciliation experience.

Key Responsibilities :

- Build and maintain automated data-quality and validation frameworks for Fabric pipelines.

- Develop checks for row counts, completeness, nulls, duplicates, referential integrity, aggregations, variance, data freshness, latency, schema drift, and data anomalies.

- Build automated source-to-target reconciliation between legacy and Fabric environments.

- Compare row counts, aggregates, column values, and sampled records to identify migration issues.

- Investigate data discrepancies and perform root-cause analysis across source, transformation, and target layers.

- Validate incremental loads, CDC, historical loads, and late-arriving data.

- Develop and maintain regression test suites for migrated data objects and transformations.

- Validate Power BI semantic models, measures, KPIs, relationships, and report outputs.

- Support QA, UAT, defect management, and release-readiness activities.

- Integrate data-quality checks into CI/CD pipelines where required.

- Monitor data quality across completeness, accuracy, consistency, conformity, uniqueness, and timeliness.

- Work closely with Data Engineers, Architects, Analytics, QA, and business stakeholders.

Required Skills :

- 4+ years of experience in Data Engineering, Data Quality, Data Validation, ETL Testing, or related areas.

- Strong SQL, including joins, CTEs, window functions, aggregations, and reconciliation.

- Strong Python and PySpark experience.

- Hands-on experience with Microsoft Fabric, including Lakehouse, Warehouse, Notebooks, and Pipelines.

- Experience with Azure Data Factory, Azure Synapse, or Databricks is also relevant.

- Experience building automated data validation/testing frameworks.

- Strong experience with source-to-target reconciliation and data migration testing.

- Working knowledge of Power BI and semantic model validation.

- Experience with Git, Azure DevOps, CI/CD, and code reviews.

- Strong troubleshooting and root-cause analysis skills.

- Experience working with distributed/global teams and excellent communication skills.

Preferred Skills :

- Experience with legacy warehouse-to-Fabric/Azure/Databricks migrations.

- Microsoft Fabric certifications such as DP-700 or DP-600.

- Experience with Microsoft Purview Data Quality.

- Experience with Delta Lake, CDC, anomaly detection, or data observability tools.

- Experience working with large datasets and optimizing data validation workloads.

- Experience with Agile/Scrum delivery.