Quality Engineering Lead

Requirements

• Bachelor's degree in Computer Science, Engineering, or related field
• Extensive experience (typically 15+ years) in testing, quality engineering or data delivery roles, with a strong technical background
• BPSS minimum, SC not mandated but strong likelihood to be required
• Proven Director level experience leading testing and quality engineering on large, complex data-centric programmes
• Excellent stakeholder management skills, with the ability to influence at Programme Board level and to convey complex technical concepts to both technical and non-technical stakeholders
• Experience managing large SDET and QA teams, including test data and environment management
• Demonstrated experience producing Programme-level Test Strategies and Master Test Plans for functional and non-functional testing in a data environment with a strong focus on data quality and integrity
• Strong understanding of data engineering concepts, including data pipelines, data migration, data architecture and related testing methodologies
• Experience leading teams responsible for designing, building and maintaining automated data testing frameworks, leveraging scripting and orchestration where appropriate
• Strong problem-solving skills and ability to think critically
• Proven experience delivering direction, guidance and hands-on input into test data testing using scalable robust automation frameworks
• Ensuring automated test coverage across ETL / ELT pipelines and data transformations
• Guiding SDETs on automation of bulk and delta data load testing
• Demonstrated oversight of test data creation, synthetic data management and test environment coordination
• Experience managing test coverage, defect management and quality metrics, ensuring results are measurable, traceable and auditable with common tooling such as JIRA, Azure DevOps, XRAY (or equivalent)
• (Desirable) Experience of data feeds architectures and best practices using AWS cloud services delivering Data Warehouse to Data Lakehouse migrations
• (Desirable) Relevant AWS certifications (e.g. AWS Certified Data Engineer – Associate/Professional)
• (Desirable) Strong knowledge of AWS cloud services and data architectures
• (Desirable) Experience with leveraging AI accelerators in data processing or testing
• (Desirable) Hands on or oversight experience with API testing, Python/PySpark scripting, Oracle, SQL and YAML
• (Desirable) Knowledge of Non-Functional Testing (Performance, security, isolation, scalability)
• (Desirable) Knowledge of CI/CD pipelines, including GITHUB Actions (or similar)

What the job involves

We are seeking an experienced Test Director / Quality Engineering Director with a deep technical background to join the Programme Leadership Team of a large, complex, multi-supplier data transformation programme. This role requires a technologist first and foremost, providing authoritative technical oversight and independent assurance of data solutions delivered on AWS-based platforms from someone who has progressed into senior leadership but retains strong hands-on understanding and credibility across data engineering, cloud platforms and modern data architectures.

Operating at Director level, the role will guide and monitor quality, engineering standards, governance and ways of working across multiple delivery partners, while engaging confidently with senior client stakeholders. The successful candidate will bring substantial experience leading technical testing at scale, with the ability to deep-dive into architecture, data pipelines and automation when required, while also operating effectively at programme and executive level.

The QAE Lead is accountable for end-to-end quality assurance and testing strategy, ensuring that data solutions delivered on AWS-based data platforms meet business, regulatory and operational expectations.

Programme-Level Accountability & Leadership

• Sit as a full member of the Programme Leadership Team, accountable for quality outcomes across the delivery
• Act as the single point of accountability for all testing and quality engineering activity
• Provide clear, evidence-based recommendations on go-live readiness, data integrity and operational risk
• Lead, coach and motivate a team of Test Leads and SDETs

Multi-Supplier & Stakeholder Management

• Serve as the primary point of contact for the client and third-parties on all testing matters
• Coordinate testing across multiple suppliers, ensuring consistency in standards, tooling and reporting
• Challenge delivery partners constructively on test coverage, defect quality, dependencies and risk exposure
• Communicate complex technical test outcomes in clear business terms to senior stakeholders

Test Strategy & Operating Model

• Define, own and maintain the Test Strategy, aligned to a Data Factory testing model
• Author and assure Programme-level Master Test Plans and Test Phase / Test Level Plans
• Establish and govern the end-to-end test operating model, including vendor integration, dependencies and hand-offs
• Define quality gates, entry/exit criteria, test coverage expectations and acceptance thresholds

Governance, Reporting & Assurance

• Establish and run testing governance forums, cadences and escalation paths
• Implement and maintain quality policies, controls and assurance mechanisms, including Test Exit Reports
• Monitor, track and report progress across multiple teams using clear, executive-level dashboards and metrics
• Ensure testing artefacts and outcomes are auditable and support internal/external quality and audit reviews

Technical Quality & Data Testing Oversight

• Maintain deep understanding of the AWS infrastructure, data platforms and data products/pipelines under test
• Oversee and assure testing across multiple test levels
• Lead SDET teams testing bulk and delta data loads, reconciliation and data quality outcomes
• Provide assurance over ETL / ELT pipelines testing, data migrations and platform repointing activities

Engineering, Automation & Innovation

• Lead core programme SDETs responsible for testing AI-enabled and automated data pipelines
• Promote pragmatic use of automation solutions and AI to improve coverage, quality and efficiency at scale
• Oversee testing of AWS-based Lakehouse architectures, including: AWS Glue, Apache Iceberg, AI ETL accelerators, Neo4j, Python / PySpark, SQL and YAML-driven configurations