Quality Engineering Lead

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 pipeline 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.

What experience you'll bring

• 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 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.
• Defining automated test approaches for data validation, reconciliation and data quality rules.
• Ensuring automated test coverage across ETL / ELT pipelines and data transformations.
• Guiding SDETs on automation of bulk and delta data load testing.
• Demonstrating oversight of test data creation, synthetic data management and test environment coordination.
• 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).

Preferred Skills and Qualifications

• Experience with data feed architectures and best practices using AWS cloud services delivering Data Warehouse to Data Lakehouse migrations.
• Relevant AWS certifications (e.g. AWS Certified Data Engineer – Associate/Professional).
• Strong knowledge of AWS cloud services and data architectures.
• Experience with leveraging AI accelerators in data processing or testing.
• Hands‑on or oversight experience with API testing, Python/PySpark scripting, Oracle, SQL and YAML.
• Knowledge of Non‑Functional Testing Performance, security, isolation, scalability.
• Knowledge of CI/CD pipelines, including GitHub Actions (or similar).

Equal Opportunities Statement

We are an equal opportunities employer. We believe in the fair treatment of all employees and commit to promoting equity and diversity in our employment practices. We are also a proud Disability Confident Committed Employer and commit to creating a diverse and inclusive workforce. We actively collaborate with individuals who have disabilities and long‑term health conditions that may affect their daily activities, ensuring that barriers are eliminated when it comes to employment opportunities. In line with our commitment, we guarantee an interview to applicants who declare to us during the application process that they have a disability and meet the minimum requirements for the role. If you require any reasonable adjustments during the recruitment process, please let us know.