lendable - Mobile Quality Engineer
Requirements
• Hands-on experience with Maestro or Detox. • 2-3+ years in quality engineering or test automation, with a track record of keeping automated suites stable rather than letting them rot into a graveyard of quarantined tests. • Experience introducing AI-assisted testing practices (test generation, failure clustering, coverage analytics) with sensible controls around review and trust. • Strong TypeScript and JavaScript - not just writing test scripts, but building helpers, tooling, and debugging into the app when needed. • Experience shaping CI/CD pipelines for test reliability (GitHub Actions or similar), including flakiness reduction, parallelisation, artefact management, and runtime control. • Comfortable with mobile E2E automation, or strong E2E experience on another platform plus a credible plan for getting productive on mobile quickly. • Able to operate independently and drive improvements end-to-end (frameworks, CI, test data, reporting). • Hands-on experience testing React Native apps. • Mobile performance and reliability work (crash reporting, startup time, memory and battery awareness). • API and contract testing experience, with a point of view on test data and environment strategy. • Mobile: TypeScript, React Native, Expo, EAS, GraphQL, Relay, Jest, React Testing Library, Maestro. • Backend: Kotlin, PHP 8 (Symfony), AWS, Postgres, RabbitMQ, Docker, Kubernetes. • Tooling and observability: GitHub, GitHub Actions, Jira, Confluence, Datadog, Sentry, Grafana.
Responsibilities
• Own the mobile test strategy: • Define and evolve a pragmatic test pyramid for React Native - deciding where E2E is worth the weight and where logic belongs in faster layers (Jest, React Testing Library, component tests). • Make deliberate calls about coverage, reliability, and speed trade-offs across the app. • Set the bar for what "ready to ship" looks like on mobile, and hold the line on it. • Build reliable E2E automation: • Create and maintain mobile E2E suites in Maestro, including the scaffolding around them - test data, device configuration, helpers, reporting. • Integrate suites into CI (GitHub Actions) so engineers get fast, trustworthy feedback on every PR and release candidates carry a signal people actually believe. • Drive flakiness down and time-to-diagnosis down. We treat a flaky test the same way we treat a broken test, and we expect root-cause work rather than retry-until-green. • Bring AI into the SDLC: • Use AI tools day-to-day to accelerate test authoring, cluster failures, surface coverage insights, and propose fixes. • Decide where AI adds leverage and where a human eye is needed. A suite that looks comprehensive but isn't trustworthy is worse than a smaller one we rely on. • Help shape how the mobile team uses AI-assisted workflows more broadly - prompts, guardrails, review patterns. • Prevent defects, not just catch them: • Partner with product and engineering on shift-left quality: join specs early, push back on ambiguous acceptance criteria, and surface risk before code is written. • Close the loop on production issues using our observability stack (Datadog, Sentry, Grafana) - tying test coverage back to real customer impact. • Run targeted exploratory testing on high-risk releases when it's the right call.
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