QA Architect

About the Role:

We are looking for an experienced QA Architect to define and drive the technical direction of quality engineering across our organisation.

You will work closely with our existing SDET and engineering teams to establish a scalable and consistent quality engineering architecture, strengthen shift-left testing practices, and improve the efficiency and reliability of our testing ecosystem.

This is a hands-on individual contributor role, not a people management position. You will act as the technical authority for quality engineering — defining standards, reviewing test architecture and frameworks, guiding senior SDETs, and leveraging AI and modern engineering practices to improve testing efficiency and software quality.

Job Responsibilities:

1. Test Strategy & Architecture

• Define and drive the organisation’s quality engineering and test automation strategy.
• Establish standards across unit, integration, contract, API and end-to-end testing.
• Define best practices for test framework architecture, code quality, maintainability and flaky test management.
• Evaluate existing and emerging QA tools and make recommendations on tooling and framework architecture.
• Conduct test architecture reviews and provide technical guidance to SDETs and engineering teams.

1. Shift-Left & Quality Engineering

• Collaborate with product and engineering teams during design and architecture stages to identify quality risks before development begins.
• Establish testability, observability and quality-by-design practices across engineering teams.
• Improve CI/CD quality feedback so defects can be identified earlier in the development lifecycle.
• Promote engineering practices that prevent defects rather than relying primarily on downstream testing.

1. AI-Driven Quality Engineering

• Identify and implement practical AI use cases across the testing lifecycle, including test generation, failure triage, flaky test classification, risk-based test selection, synthetic test data and AI-assisted test authoring.
• Evaluate and integrate AI-assisted testing capabilities into existing frameworks and CI/CD pipelines.
• Establish standards and guardrails to ensure AI-generated tests remain reliable, deterministic, maintainable and reviewable.
• Measure the effectiveness of AI adoption through improvements in test authoring effort, execution cycle time, triage time and maintenance cost.

1. Test Data, Environments & Resilience

• Define and improve test data management strategies, including provisioning, masking, anonymisation, refresh and versioning.
• Design scalable test environment strategies using approaches such as service virtualisation, contract testing and ephemeral environments.
• Establish resilience testing approaches to validate system behaviour under dependency failures, latency and disaster recovery scenarios.

1. Test Infrastructure & CI/CD

• Enhance existing test infrastructure for greater speed, scalability, parallelism and reliability.
• Define how automated testing integrates into CI/CD pipelines.
• Support containerised test execution, parallel testing and cloud-based browser/device testing.
• Continuously improve pipeline execution time and reduce flaky tests.

1. Quality Metrics & Technical Influence

• Define and track meaningful quality engineering metrics, including escaped defects, cycle time, MTTR, automation coverage, flaky test rate and automation ROI.
• Communicate quality risks and engineering recommendations to technical and non-technical stakeholders.
• Act as a technical bridge between Quality Engineering, Engineering, Architecture and Platform teams.
• Drive adoption of quality engineering standards across teams through technical influence rather than direct authority.

Qualifications:

• 10+ years of experience in software quality engineering or software development, including 3+ years at Principal, Staff, Lead SDET, Test Architect or equivalent level.

• Strong hands-on programming skills in Python or TypeScript, with solid understanding of software design principles and design patterns.

• Proven experience designing and implementing test automation frameworks adopted by multiple engineers or teams at scale.

• Hands-on experience applying AI to quality engineering, such as AI-assisted test generation, failure triage, self-healing automation, risk-based testing, synthetic test data or AI coding assistants.

• Strong experience designing and integrating automated testing into CI/CD pipelines, such as GitHub Actions, GitLab CI, Jenkins or Azure DevOps.

• Experience with containerised environments using Docker and/or Kubernetes.

• Practical experience with modern test automation frameworks such as Playwright or equivalent, together with API and contract testing.

• Experience with test data management in large-scale or complex environments.

• Experience with at least one major cloud platform such as AWS, Azure or GCP.

• Strong technical leadership and influencing skills, with demonstrated ability to drive engineering standards across teams without direct reporting authority.

Nice to Have:

• Experience with chaos engineering or resilience testing, such as Gremlin, Chaos Mesh, Litmus or equivalent approaches.
• Experience with service virtualisation and contract testing, such as Pact, WireMock or Mountebank.
• Knowledge of security testing practices, including OWASP Top 10 and SAST/DAST tooling.
• Experience working within a global or distributed engineering organisation across multiple locations or time zones.
• Relevant industry/domain experience.
• ISTQB Advanced, cloud or other relevant technical certifications.