Data Quality Engineer

Are you interested in working with the World’s leading AI-first Quality Engineering Company? Ready to advance your career, team up with global thought leaders across industries and make a difference every day? Join us at QualityAI (Formerly known as Qualitest)!

We are looking for a Data Quality Engineer to join our growing team in United States!

We are seeking a Data Quality Engineer to join our Data Products team and help establish quality engineering best practices across a rapidly expanding data platform within the life insurance domain. This role will focus on ensuring the quality, reliability, and integrity of foundational data products that enable enterprise reporting, analytics, and digital initiatives.

As the organization accelerates the development of more than 30 data products, the Quality Engineer will play a key role in operationalizing testing practices, driving consistency across multiple delivery teams, and supporting both internal and vendor-led development efforts. The ideal candidate has strong experience in data validation, ETL testing, and data pipeline quality, with a passion for building scalable quality processes in a cloud-based data environment.

Responsibilities:

• Define and implement Quality Engineering best practices for enterprise data products.
• Develop and execute test strategies for data pipelines, ETL processes, and data transformations.
• Validate data across multiple layers of the data platform, ensuring accuracy, completeness, and consistency from source systems through curated data products.
• Perform data validation across Microsoft Azure OneLake using a Medallion Architecture (Bronze, Silver, Gold).
• Verify data movement and transformation between on-premises and Azure-based data sources.
• Partner with Data Engineers, Product Owners, Architects, Business Analysts, and external delivery partners to embed quality throughout the development lifecycle.
• Create reusable test scenarios and validation frameworks that can be adopted consistently across multiple data product teams.
• Execute source-to-target data validation, reconciliation, and transformation testing.
• Identify, document, and track defects while working closely with development teams to resolve data quality issues.
• Support regression testing for enhancements to existing data products.
• Contribute to automation efforts for data validation and ETL testing where appropriate.
• Participate in release validation and production readiness activities.
• Help establish testing standards that improve delivery velocity while maintaining data quality.

Required Qualifications:

• 5+ years of Quality Engineering or QA experience with a strong focus on data platforms.
• Experience testing ETL/ELT pipelines and data transformation processes.
• Strong SQL skills for data validation and reconciliation.
• Experience validating large datasets across multiple source systems.
• Experience working with cloud data platforms, preferably Microsoft Azure.
• Understanding of modern data architectures, including data lakes and Medallion Architecture (Bronze, Silver, Gold).
• Experience testing data ingestion, transformation, and data integration workflows.
• Strong analytical and troubleshooting skills.
• Experience working in Agile development environments.

Preferred Qualifications:

• Experience with Azure Data Factory, Azure Synapse Analytics, Microsoft Fabric, or OneLake.
• Experience validating enterprise reporting and analytical data products.
• Knowledge of Customer 360 or master data management concepts.
• Experience with test automation for data validation.
• Familiarity with notebook orchestration, scheduled pipelines, and data workflow automation.
• Experience working alongside offshore or vendor development teams.

Technology Environment:

• Microsoft Azure
• Azure OneLake / Microsoft Fabric
• Medallion Architecture (Bronze, Silver, Gold)
• SQL
• ETL/ELT Pipelines
• Data Warehousing
• On-premises and cloud data sources
• Data validation and reconciliation frameworks
• Agile delivery

What You''ll Be Working On:

• Building quality practices for a growing portfolio of 30+ enterprise data products.
• Supporting foundational data products that standardize interfaces across multiple business domains.
• Validating enterprise data used for executive reporting, analytics, and digital initiatives.
• Ensuring reliable movement of data from operational systems into Azure-based analytical platforms.
• Establishing scalable quality processes that support long-term product enhancements and future development.