QA Lead (ETL Testing)

Experis to światowy lider rekrutacji specjalistów i kadry zarządzającej w kluczowych obszarach IT.

• Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related field.
• 10+ years of experience in Software Testing with at least 5+ years in QA leadership roles.
• Proven experience leading ETL/Data Testing programs in Agile delivery environments.
• Strong stakeholder management, communication, and leadership skills.
• Experience managing geographically distributed teams and cross-functional programs.
• Strong analytical, problem-solving, and decision-making capabilities.

Preferred Certifications:

• ISTQB Advanced Test Manager / Test Analyst
• Certified Scrum Master (CSM) or SAFe Agile Certification
• Microsoft Azure Data Engineer Associate
• Databricks Fundamentals Certification
• AI/ML Testing or Data Quality related certifications

• Lead end-to-end QA activities across data integration, ETL/ELT, data warehouse, analytics and AI-enabled solutions, ensuring data quality, accuracy, integrity and compliance.
• Define and drive test strategies, plans, quality gates, risk assessments and release readiness across Agile delivery teams.
• Lead testing across the full data lifecycle from source data and ingestion through transformation, Common Data Model (CDM) mapping, publication and downstream consumption.
• Oversee functional, integration, regression, UAT, performance and automation testing, ensuring appropriate coverage and traceability.
• Establish and govern data validation, reconciliation, data quality controls and Ground Truth datasets.
• Lead defect management, root-cause analysis and quality improvement with Development, Product, Business and Architecture teams.
• Manage QA resources, workload and priorities across multiple releases and sprint cycles, providing mentoring and guidance.
• Define and evolve ETL test automation and data validation frameworks, including reusable regression solutions and CI/CD integration.
• Ensure test evidence, data lineage, observability and auditability throughout testing lifecycle.
• Lead non-functional testing of data pipelines, including performance, scalability, resilience, recovery and data-processing reliability.
• Support validation of financial and regulatory data with Business and Accounting SMEs.
• Define and execute QA approaches for AI/LLM and agentic AI workflows, including output validation, hallucination detection, traceability, human-in-the-loop controls and reproducibility.
• Provide quality status, risk and release-readiness reporting to stakeholders and leadership.