Senior Automation QA Engineer (SDET)

We are looking for a senior automation QA engineer to own automated testing across our web and mobile products, and to raise the engineering quality bar across the team.

The split is roughly 70 / 30:

• 70% test automation - Playwright (web) and Maestro (mobile) suites, API and integration tests, test data and environments, CI quality gates, turning production failures into permanent checks.
• 30% product engineering - you write real code in the same repos the team ships from: unit and integration tests next to the developers, internal tooling, and small to medium features or bug fixes.

This is not a manual QA role, and not script-writing in isolation. We are looking for an engineer who happens to specialise in quality: someone who can pick up a feature ticket, implement it with tests, and pass code review.

Our stack

• Languages: TypeScript, JavaScript
• Backend: Node.js, Express
• Frontend: React, React Native
• Databases: PostgreSQL, MongoDB
• Infrastructure: AWS
• Testing: Playwright, Maestro, Jest / Vitest
• CI/CD: GitHub Actions

Test automation and quality engineering (~70%)

• Own the Playwright and Maestro end-to-end suites: fast, stable, parallelised, and honest about why a test failed.
• Design the automation strategy across the pyramid instead of pushing everything to the UI layer.
• Build API and integration coverage for Node.js / Express services, including auth, error paths, and data consistency.
• Own test data and environments: fixtures, factories, seeding, resets, and isolation between runs.
• Maintain quality gates in GitHub Actions, with useful artifacts: traces, videos, screenshots, logs.
• Treat flakiness as a first-class task: find root causes, fix waits and race conditions, track suite reliability as a metric rather than adding retries.
• Build reusable automation infrastructure: abstractions, custom fixtures, mocks, service virtualisation, shared helpers.
• Turn every escaped bug or production incident into a regression test.
• Report quality clearly: what is covered, what is failing and why, and what it means for a release decision.
• Join design and refinement early, so features are testable before they are built.
• Run targeted exploratory testing where automation is not yet the right tool.

Product engineering and feature work (~30%)

• Write unit and integration tests for production code in Jest or Vitest - real assertions and edge cases, not coverage padding.
• Implement small to medium features and bug fixes in Node.js, React, or React Native, to the same review standards as the rest of the team.
• Refactor for testability: break up tightly coupled code and introduce seams without changing behaviour.
• Review other engineers' code with specific, actionable feedback on test quality, error handling, and failure modes.
• Build internal tooling that shortens the team's feedback loop: scripts, CLI utilities, dashboards, mock services.
• Coach engineers on testing, error handling, retries, idempotency, and failure isolation, so quality is not owned by one person.
• Help troubleshoot production issues: read logs and traces, reproduce, drive to resolution. Occasional incidents outside working hours.

What we're looking for

• 4+ years in test automation, SDET, or software engineering, owning automated testing for a production web or mobile product.
• Strong JavaScript and TypeScript: typed, maintainable code, and a solid grasp of async behaviour, promises, and timing issues.
• Software engineering fundamentals: Git workflows, code review, debugging, design patterns, SOLID, and how HTTP, REST, auth, and async systems actually work.
• Unit and integration testing, not only end-to-end: mocking, test doubles, fixtures and factories, parameterised tests, and knowing when a unit test is the wrong tool.
• Owning a Playwright, Cypress, or equivalent suite at scale, including parallel execution and flakiness reduction.
• REST API testing: tooling, contract or schema validation, negative and edge cases.
• Reading and contributing to a production Node.js and React codebase: navigate unfamiliar code, trace a bug to its source, ship a fix with tests.
• Working SQL and PostgreSQL: query and verify data, understand schemas and migrations, use the database to set up and assert test state.
• Building CI/CD pipelines in GitHub Actions or equivalent: caching, parallel jobs, artifacts, and keeping pipeline time under control.
• Debugging across frontend, backend, database, and infrastructure.
• Clear async written communication: bug reports and test results people can act on without a meeting.

AI-assisted engineering (required)

We build AI features and use AI tooling daily. You do not need to be an ML engineer, but you should already work this way:

• Daily use of frontier models and agentic coding tools (Claude Code, Codex, Cursor) for tests, fixtures, refactoring, and code investigation.
• A repeatable way of giving agents context - instruction files, tool definitions, MCP servers, subagent roles - and the judgement to review output instead of merging it blindly.
• Understanding that AI-driven features cannot be validated by conventional assertions alone, and willingness to grow into evaluation work: golden datasets, scoring, regression gates.
• A practical sense of token spend and wall-clock time per unit of delivered work.