Senior Quality Engineer

Lendable is a UK-based fintech company with over 700 employees, profitable since 2017, and backed by investors including Balderton Capital and Goldman Sachs. The company has rebuilt major consumer finance products such as loans, credit cards, and car finance, aiming to expand in the UK and US markets. The role is within the Mobile team working on the Zable app, which integrates multiple financial products and is built with React Native, TypeScript, and Kotlin backend.

Essential:

• 5+ years in quality engineering or test automation with a strong track record of maintaining stable automated test suites.
• Proficient in TypeScript and JavaScript for building helpers, tooling, and debugging.
• Experience with Appium, device farms, or emulator/simulator orchestration at scale.
• Hands-on experience with Maestro or Detox.
• Experience shaping CI/CD pipelines for test reliability using tools like GitHub Actions.
• Comfortable with mobile E2E automation or strong E2E experience on other platforms with a plan to ramp up on mobile.
• Ability to operate independently and drive end-to-end improvements.

Desirable:

• Hands-on experience testing React Native apps.
• Experience with mobile performance and reliability (crash reporting, startup time, memory, battery).
• API and contract testing experience with insights on test data and environment strategy.
• Knowledge of native mobile fundamentals (Swift or Kotlin).
• Prior experience introducing AI-assisted testing practices with sensible review controls.

Own the mobile test strategy:

• Define and evolve a pragmatic test pyramid for React Native, balancing E2E and faster test layers.
• Make deliberate decisions on coverage, reliability, and speed trade-offs.
• Set and enforce the "ready to ship" quality standards for mobile.

Build reliable E2E automation:

• Create and maintain mobile E2E suites in Maestro, including test data, device configuration, helpers, and reporting.
• Integrate test suites into CI (GitHub Actions) for fast, trustworthy feedback.
• Reduce test flakiness and time-to-diagnosis through root-cause analysis.

Bring AI into the SDLC:

• Use AI tools daily to accelerate test authoring, cluster failures, surface coverage insights, and propose fixes.
• Determine appropriate use of AI versus human review to maintain trustworthy test suites.
• Help shape AI-assisted workflows including prompts, guardrails, and review patterns.

Prevent defects, not just catch them:

• Collaborate with product and engineering on shift-left quality by joining specs early and clarifying acceptance criteria.
• Close the loop on production issues using observability tools (Datadog, Sentry, Grafana).
• Conduct targeted exploratory testing on high-risk releases when appropriate.