Data Quality Engineer

Data Quality Engineer

The Data Quality Engineer will ensure the accuracy, consistency, and reliability of data across the Microsoft Fabric Data Warehouse, its integrations, and connected services. The role focuses on testing and validating data pipelines, event-driven processes, and Azure-based integrations such as Logic Apps and Service Bus. The engineer will collaborate closely with data engineers, architects, and business units to maintain high-quality, trustworthy data for analytics and reporting.

Key responsibilities include designing, developing, and executing data quality and validation tests for Microsoft Fabric Data Warehouse objects (tables, views, semantic models); validating ETL/ELT pipelines, including Fabric Dataflows and integrated source systems, for accuracy and completeness; testing and monitoring event-driven data processes, including Azure Service Bus messages, queues, and topics; validating Azure Logic Apps workflows and integrations that move or transform data between systems; performing end-to-end testing of data ingestion, transformations, and delivery to downstream consumers or BI systems; developing and maintaining automated data quality checks, reconciliation scripts, and test frameworks; identifying, analyzing, and reporting data quality issues, including root cause analysis and impact assessment; collaborating with Data Engineers, BI teams, and business units to define data quality rules, metrics, and acceptance criteria; monitoring data quality KPIs, implementing controls to prevent defects, and ensuring reliability in production pipelines; and documenting test cases, workflows, data quality rules, and testing outcomes clearly for technical and business stakeholders.

Required skills and qualifications include strong experience in data quality testing and validation for data warehouses or large-scale analytics platforms; hands-on experience with Microsoft Fabric (Data Warehouse, Lakehouse, Pipelines, Dataflows); proficiency in SQL and experience validating ETL/ELT pipelines; experience with event-driven architectures and Azure integrations; experience testing data integrations across multiple source systems; familiarity with data quality concepts: completeness, accuracy, consistency, timeliness, uniqueness; experience with automation and scripting (Python, PySpark, PowerShell); understanding of data modeling concepts: star schema, snowflake, fact and dimension tables; strong analytical, problem-solving, and troubleshooting skills; excellent documentation and communication skills; and familiarity with unit testing, automated data validation, and integration testing tools such as dbt, Great Expectations, tSQLt, pytest, Postman, Azure Logic Apps testing, and Service Bus testing scripts.

Preferred/nice to have includes experience with data quality frameworks (e.g., Great Expectations); knowledge of Azure Data Factory, Synapse Analytics, and OneLake; familiarity with CI/CD pipelines for data platforms; and understanding of data governance, metadata management, and auditing standards.

Soft skills include strong analytical skills and capacity to challenge the financial information received; high sense of organization and ability to manage multiple tasks with strong attention to detail; excellent communication skills with the ability to interact with international stakeholders; curiosity, proactivity, keenness to learn, and readiness for new challenges; and ability to work independently while also having a team-oriented mindset.

Languages include excellent knowledge of English (written and verbal communication skills) and knowledge of any other language is a plus (French).