Sr QA Engineer

RYZ Labs

11d•Remote

About The Position

RYZ Labs is looking for a Senior QA Engineer to join one of our client's team. You will own quality across a React Native mobile app and a Next.js web experience, backed by Java/Spring Boot services behind a Node BFF. The work is hands-on: real devices, developer tools, logs, and APIs, not only clicking through the UI. The single most important non-technical trait we are hiring for is clear communication.

Requirements

Browser developer tools. You are comfortable in the developer console. You can read a JavaScript error, inspect a failing network request and its payload, check status codes and headers, poke at local/session storage, and emulate a device viewport. You use it to investigate, not just to screenshot.
React Native application debugging. You have tested and debugged a React Native app on real iOS and Android devices as well as simulators and emulators. You can pull native device and crash logs, install and manage pre-release and over-the-air builds, reproduce an issue across OS versions and screen sizes, and tell the difference between "broken on this device," "broken everywhere," and "broken in the JavaScript layer, not the native one.
Test automation ownership. You have designed and built automated test suites from scratch, not just extended someone else's: Playwright for web, Maestro for mobile. Our web checkout flows already run on Playwright. You know what to automate vs. cover manually, and how to keep a suite from rotting into flaky noise.
API testing with Postman or comparable tools. Most of our services are Java/Spring Boot REST APIs sitting behind a Node BFF.
Basic SQL (Postgres) and comfort reading structured application logs.
Observability tooling. Familiarity with Datadog or a comparable platform for logs, dashboards, and error tracking. You can search logs for a request, follow an error, and use what you find to make a bug report stronger.
Strong communication. You write clearly, you explain your reasoning, you ask questions when something does not make sense, and you close the loop. This is the single most important non-technical trait we are hiring for.
CI/CD integration. You have wired automated tests into a pipeline (GitHub Actions or comparable) so they gate builds/deploys, and you know how to triage and reduce flaky tests rather than let a team start ignoring red pipelines.
AI-assisted workflows. You are actively using AI tooling in your day-to-day work and can help the team push further in that direction.

Responsibilities

Execute manual and exploratory test passes against web and mobile releases, working from acceptance criteria, designs, and your own curiosity about where things break.
Use browser developer tools (console, network, elements, application storage, device emulation) as a first-line diagnostic tool, not just to confirm a bug but to narrow down where it lives.
Debug our React Native application on real iOS and Android devices as well as simulators and emulators: reading native device and crash logs, using React Native's debugging tooling (Metro, the JS console, React DevTools), inspecting network traffic, working with pre-release and over-the-air builds, and reproducing issues across OS versions and device classes.
Use Datadog (logs, dashboards, error tracking, RUM/session replay) to triage reported issues, confirm whether a bug is client-side or server-side, and attach real evidence to a ticket.
Test REST APIs directly, not just through the UI, validating request/response payloads, status codes, error handling, and edge cases for the Java/Spring Boot services and Node BFF.
Validate auth/authorization and role-based access at the API level, not just what is visible in the UI.
Test service-to-service and data-pipeline behavior, e.g. confirming events land correctly downstream rather than only checking the originating call succeeded.
Test idempotency, retries, and failure/edge-case handling in backend logic.
Write bug reports another engineer can act on without a follow-up conversation: clear repro steps, environment, expected vs actual, logs, network calls, screenshots or recordings.
Build and maintain regression suites and release checklists, and help move repetitive coverage toward automation over time.
Leverage AI-assisted tooling to speed up test case generation, log analysis, and repro investigation.