Quality Assurance Engineer II

Job Title: Quality Assurance Engineer II

Role Summary

We are looking for a QE Engineer to join one of our product squads at Pearson. This is a hands-on role that combines manual and automated testing across web and API layers. You will work closely with developers, product managers, and designers to ensure features are tested thoroughly, bugs are communicated clearly, and quality is maintained throughout delivery.

This role suits someone with 3–4 years of QE or testing experience who can operate independently across manual and automated testing, make confident scoping decisions, and contribute to the team’s automation strategy. You will own quality for the features you work on, with a senior QE engineer or lead available for guidance on complex cross-team or architectural decisions.

Key ResponsibilitiesTest Strategy & Execution

• Independently scope test coverage for assigned features — deriving scenarios from the story, Figma designs, acceptance criteria, and integration dependencies without needing to be directed

• Make deliberate decisions about what to test, what to skip, and why — document the rationale, not just the outcome

• Identify integration touch points and cross-service dependencies that affect test coverage, and raise them before development completes

• Raise testability concerns during story refinement or design review — not after testing has begun

• Communicate testing status, risks, and coverage gaps clearly to developers, PMs, and the wider team

Automation

• Own UI automation for features you test — write, run, and maintain Playwright tests for critical user journeys from scratch, not just modifying existing ones

• Build and maintain API automation test suites — covering happy path, negative path, auth scenarios, response body assertions, and error handling

• Create dynamic test data via API calls rather than relying on hardcoded values; understand the impact of shared or stale test data on suite reliability

• Debug automation failures systematically — isolate root cause from logs, distinguish product bugs from script issues, and fix rather than work around

• Proactively identify and address flaky tests — investigate root cause and raise a ticket or fix rather than silently re-running

• Push MRs with clean, reviewable automation code; monitor your own pipeline results and resolve failures independently

AI-Assisted Testing

• Own test coverage for AI-assisted features as part of your sprint delivery — validate outputs for accuracy, consistency, business correctness, and edge-case behaviour