middle qa engineer

ABOUT THE CLIENT

Our client is a leading professional services firm providing audit, tax, strategy, transaction, and consulting services to clients in the financial services sector.

The Digital Factory team drives technological innovation and digital transformation initiatives across service lines, supporting critical business operations while maintaining the highest regulatory compliance and security standards. Undergoing digital transformation. They specialize in implementing advanced AI solutions across their business operations and are committed to leveraging cutting-edge technology to drive innovation and efficiency.

PROJECT DETAILS

Our client is seeking a talented and motivated QA Engineer to join our dynamic Digital Factory team at Luxembourg. In this role, you will analyze, develop, support, and maintain applications. You will collaborate with stakeholders to gather feedback, ensure the application performs as expected in the production environment, and maintain high levels of customer satisfaction.

YOUR TEAM

Our Digital Factory team provides access to the latest technology, data capabilities and infrastructure to accelerate innovation. Digital Factory Luxembourg is a diverse and international group of digital experts, including designers, developers, AI specialists, and business professionals, focused on creating valuable digital solutions for our clients and stakeholders.

WHAT'S IN IT FOR YOU

• Interview process that respects people and their time
• Professional and open IT community
• Internal meet-ups and resources for knowledge sharing
• Time for recovery and relaxation
• Bright online and offline events
• Opportunity to become part of our internal volunteer community

RESPONSIBILITIES

Test Planning & Design

• Collaborate with business analysts, data engineers, and developers to understand data requirements and transformations.
• Define test case suites and approaches for data validation in ETL, data pipelines, APIs, and reporting systems.
• Create and maintain test cases and test data sets for structured and unstructured data.

Data Validation & Testing

• Perform validation of source-to-target data mapping, ensuring data integrity, accuracy, and completeness.
• Verify transformations, aggregations, and calculations applied during ETL or data processing.
• Conduct cross-system comparisons (databases, APIs, warehouses, dashboards).
• Perform boundary, negative, and edge case testing for data flows.

Defect Management

• Log, track, and prioritize defects related to data discrepancies.
• Work with development and data engineering teams to troubleshoot and resolve issues.
• Re-test fixes and ensure regression coverage.
• Monitoring & Continuous Improvement
• Monitor production data pipelines for anomalies and validation failures.
• Propose improvements to test coverage, automation, and data quality checks.
• Contribute to QA best practices, documentation, and knowledge sharing within the team.

Collaboration & Communication

• Act as a bridge between QA, Data Engineering, and Development teams.
• Provide clear reporting on validation progress, risks, and issues to stakeholders.
• Support UAT (User Acceptance Testing) with business teams by clarifying data-related queries.

SKILLS

• Experience: 2–4 years of QA engineering, with at least 1–2 years focused on data validation / testing of ETL, data pipelines, or reporting systems.
• Databases & SQL: Strong knowledge of SQL (joins, aggregations, subqueries) for validating data across multiple sources.
• Data Testing: Proven experience validating large datasets, ensuring data integrity, accuracy, consistency, and completeness.
• ETL & Data Pipelines: Solid understanding of ETL processes, data warehouses, and data lake concepts.
• QA Fundamentals: Strong knowledge of manual testing principles (functional, regression, integration, negative testing).
• Defect Management: Hands-on experience with bug tracking tools (Azure DevOps) and test case management tools.
• Version Control: Basic knowledge of Git and familiarity with working in collaborative environments.
• Communication: Ability to clearly explain data-related findings to both technical and non-technical stakeholders.

• Experience validating BI/reporting tools (Tableau, Power BI, Looker, etc.).
• Exposure to cloud platforms (AWS, GCP, Azure) and their data services.
• Experience with Agile/Scrum methodologies.

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