QA Automation Engineer

We’re hiring a QA Automation Engineer to drive quality across our product suite, with our payment and billing flows as the highest-stakes surface. This isn’t a pure automation role. We want a well-rounded quality engineer, grounded in QA fundamentals and comfortable designing a test plan or exploring a feature by hand when that’s the right tool, who uses automation and AI as force multipliers, not a substitute for judgment.

You’ll design and build automation frameworks, wire quality gates into CI/CD, and use AI tools to generate, maintain, and heal tests, so your time goes where human judgment matters most. Where a payment flow can fail in production, you’ll have caught it first.

You’ll be embedded in a product pod, working alongside full-stack engineers, with a dotted line into the broader quality engineering community across our five pods.

Requirements

• 4+ years as a QA engineer, SDET, or automation engineer. You write production-grade automation code, not record-and-playback scripts, and you got there on a foundation of real testing skill.
• Solid QA fundamentals. Test design techniques (boundary analysis, equivalence partitioning, state transitions), test planning, and defect lifecycle management.
• Strong TypeScript/JavaScript. C# is a strong plus given our .NET back-end.
• Deep, hands-on Playwright experience. Selectors strategy, network interception, parallelization, CI integration, and flake management.
• Solid API testing experience. REST, auth flows, webhooks, error contract correctness (a 500 where a 400 belongs is a bug you catch), and contract testing (Pact or similar is a plus).
• Comfort testing third-party embedded UI. Stripe components, iframes, postMessage flows, and the cross-frame defects they produce.
• Working SQL skills. You can verify data state in SQL Server, not just assert on the UI.
• CI/CD fluency. You’ve built or owned pipelines, not just consumed them.
• AI-driven testing
• You already use AI coding assistants daily for test work and can demonstrate it live.
• Genuine curiosity about agentic and AI-native testing. You’ve experimented with these approaches, or you’re visibly eager to.
• Judgment about where AI output needs human review. Especially where money moves.
• Domain and quality mindset
• Experience testing payment, billing, fintech, or other correctness-critical systems is strongly preferred.
• You understand idempotency, race conditions, and async workflows well enough to design tests that catch their failure modes.
• Risk-based thinking. You know what to automate, what to explore manually, and what to skip.
• Strong communication. Your bug reports, test plans, and failure analyses are clear enough that engineers act on them without a meeting.

Nice to have

• Performance and load testing (k6, Gatling, JMeter).
• Visual regression tooling and accessibility testing (axe, WCAG).
• Docker, AWS, and testing against containerized environments.
• Prior work in a pod or squad model embedded with developers.

Responsibilities

• Design, build, and own test automation frameworks for web UI (Playwright preferred) and APIs (REST, contract tests at service boundaries).
• Build integration and e2e coverage for payment-critical flows. Hosted and iframe-embedded payment pages, merchant onboarding, webhooks, recurring billing, refunds, and reconciliation. These tests are the safety net for money movement.
• Cover multiple payment rails and regions. Cards with 3DS, ACH and eCheck in the US, PAD / ACSS debit in Canada, and surcharging rules that differ by card type.
• Own test data management. Seeding, isolation, teardown, and realistic finance scenarios (partial refunds, declined cards, ACH returns, disputed charges, trust vs operating account routing).
• Test feature-flag and entitlement matrices. The same flow can behave differently by region, brand, processor routing, and package tier, and your coverage has to prove all of them.
• Wire quality gates into CI/CD (TeamCity, GitHub Actions). Fast, reliable, deterministic suites that engineers trust and don’t bypass. Track and drive down flake; a flaky suite is a broken suite.
• Make AI the multiplier
• Use Claude, Claude Code, and similar tools daily to generate test cases, scaffold suites, refactor brittle tests, and analyze failures.
• Build AI-assisted workflows for test generation from acceptance criteria. A well-written ticket should produce a draft test suite, not a blank file.
• Evaluate and adopt AI-native testing approaches. Self-healing locators, agentic exploratory testing, AI-driven failure triage, and visual regression analysis.
• Measure the gains. Coverage growth, authoring speed, escape rate, and time-to-triage, and report them.
• Share what works with QA and QE engineers across all pods. You raise the AI-fluency bar for the whole quality org.
• Engineer quality into the SDLC
• Shift quality left. Review acceptance criteria for testability before development startas and flag ambiguity and missing edge cases early.
• Define test strategy with the pod. What gets unit, integration, contract, e2e, and what risk-based exploratory testing on top.
• Run hands-on exploratory testing on new features and high-risk changes. Automation proves what you expected; exploration finds what you didn’t.
• Test manually where it’s the right tool. New UI before automation exists, complex edge cases, and usability checks.
• Own the regression, E2E, and UAT cycles that gate payment releases. GA doesn’t ship until your suites say it can, and rollback plans are tested, not theoretical.
• Build observability into the test layer. When a suite fails, the failure should explain itself.
• Contribute performance and load baselines for payment endpoints and watch for regressions.