Quality Engineer - Mid-Level

Role overview Support software quality on a long-term client project as a mid-level quality engineer. You will independently plan and run functional testing, document and investigate defects, verify fixes, and contribute to reliable releases across mobile and other application surfaces. The engagement is freelance, approximately 40 hours per week, expected to last at least six months depending on the project, and based in Latin America.

Responsibilities - Create and maintain test plans, cases, and test data from product requirements and design documentation. - Execute functional, usability, regression, and release-readiness checks across relevant platforms and devices. - Identify, reproduce, prioritize, and track defects; investigate causes and communicate findings clearly. - Run UI or API automated tests where needed, review results, and report testing outcomes to stakeholders. - Build and maintain mobile UI automation for iOS and Android, and connect suites to CI/CD pipelines. - Triage pipeline failures, share testing practices with teammates, and suggest improvements to quality processes.

Requirements - At least three years of quality engineering experience. - Practical skill in functional test design and execution, defect reporting, and regression testing. - Ability to analyze complex scenarios, investigate problems, and work independently with attention to detail. - Familiarity with test case management and bug tracking tools, plus UI or API automation frameworks. - Hands-on experience with mobile UI automation for iOS and Android and integrating automated tests into CI/CD, including reporting and failure analysis. - Strong written and spoken English for collaboration with teammates, clients, and stakeholders; adaptable and collaborative working style.

Nice to have - Experience with mobile automation tools such as Maestro, Appium, Patrol, or Flutter integration tests; CI platforms such as GitHub Actions, Bitrise, or Codemagic; and device services such as BrowserStack. - Practical use of AI assistants or agents in QA, including creating workflows or reusable skills and checking generated results before relying on them.