We're looking for a Staff Software Engineer in Test to raise the quality bar across our mobile app and backend domain - not by owning a test suite, but by changing how the whole domain designs, ships, and trusts its software.
This is a hands-on technical leadership role. You'll shape architecture for testability and reliability, own the quality gates that let teams release with confidence, and build a culture where engineers own quality rather than hand it off. You'll operate as a true Staff Engineer: setting technical direction across several teams, going deep in the codebase when it matters, and framing quality as an engineering and business outcome - not a QA phase at the end.
If you've done this in a product with real mobile scale, even better.
Your scope of work will cover 3 areas.
I. Quality architecture & engineering
- Set and own the quality and reliability strategy across the domain - from the API/contract layer through to native iOS (Swift/SwiftUI) and Android (Kotlin) clients.
- Design for testability and release confidence: quality gates inside release and store-deployment pipelines, device and OS-version fragmentation coverage, and flakiness control at scale (BrowserStack + GitLab CI).
- Keep the backend/API/contract layer strong and connect it cleanly to the mobile client layer.
- Drive resolution of quality and reliability issues that cross team boundaries within the domain.
- Get hands-on in the codebase where your depth moves the needle - prototypes, tooling, reference implementations, thorny incidents.
II. Technical leadership, coaching & culture
- Shift quality ownership left into the engineering workflow - be the person teams pull in before implementation starts, not after.
- Mentor senior engineers across the domain and accelerate real, visible growth.
- Run knowledge-sharing across the org: tech talks, workshops, reusable documentation, pair sessions; co-lead the OneTech community.
- Co-shape the domain's technical direction with the SEM, with full autonomy within the domain, and analyse quality trends from metrics to identify where to improve.
- Define quality and reliability metrics and gates, and use them to frame risk in business terms for EMs, SEMs, and PMs.
III. AI governance in engineering
- Define how AI is adopted for quality and engineering productivity in the domain — which tools, which workflows.
- Design AI-assisted workflows and set governance and data-security standards across teams.
- Assess and manage the risk of AI usage (including production-facing work), not just the productivity upside.
- Mentor engineers on responsible, effective AI use and report on its ROI.