QE Automation Lead

In this role you lead automated data testing for a multi‑vendor data transformation programme, owning the automation framework and data validation across AWS data platforms. You guide a team of SDETs, align testing with the overall strategy, and deliver auditable test results that enable confident go‑live decisions. You combine hands‑on technical work with leadership to shape scalable, AI‑augmented testing solutions. This is a chance to impact data quality and delivery for complex data factories.

• flexible work options
• Learning and Development opportunities
• tailored benefits
• focus on wellbeing
• diversity and inclusion
• equal opportunities employer

• Lead automated data testing as the Test Lead, planning, executing and reporting automation activities
• Manage and mentor a team of Test Engineers / SDETs, guiding priorities and technical direction
• Own automation scope and backlog in line with programme milestones
• Ensure testing aligns with the QE strategy and governance and maintains auditable coverage
• Oversee testing across unit, system, E2E, performance, and UAT data validation; support quality audits
• Design and implement AI‑augmented automated testing strategies for Data Factory models
• Define and maintain automation frameworks, standards and patterns for data testing
• Contribute hands‑on to automated scripts, AI utilities, validation logic and queries
• Lead testing of AWS‑based Lakehouse and data pipelines (Glue, Iceberg, Neo4j, Python/PySpark, YAML)
• Monitor, report and triage quality issues and risks; provide data‑driven insights to stakeholders
• Collaborate with Data Engineers, Platform Engineers, Product Owners and partners to ensure integrated testing solutions
• Integrate automated tests into CI/CD (GitHub Actions or similar) and manage test data (synthetic data, masking, environments)
• Support test exit decisions and readiness assessments

• Extensive experience in data engineering and test management
• Strong technical capability in Python / PySpark, SQL, Oracle, HDFS and YAML
• Experience leading test automation for data testing in large transformation programmes
• Hands‑on experience architecting scalable automated data testing frameworks with reconciliation/validation
• Knowledge of AI‑driven testing solutions and accelerators
• Deep understanding of AWS data pipelines, AWS Glue, Data Warehouse/Lakehouse, ETL/ELT testing
• Experience building feature‑rich test automation frameworks with OO and scripting languages
• Proficiency across multiple tech stacks (JavaScript/TypeScript, Java, C#, Python)
• Experience writing Test Plans for data quality focused testing
• Strong problem‑solving and communication skills
• Experience with JIRA, XRAY, ADO for test management

• Strong communication
• Collaborative mindset
• Analytical thinking
• Python / PySpark
• SQL
• AWS Glue