QA Engineer

Job Description:

• Create quality metrics to track product improvement by developing a standard test-case document for execution.
• Execute quality improvement testing and activities (manual and automated testing).
• Develop quality assurance standards and company processes.
• Create reports that document errors and issues to be addressed.
• Collaborate with the product and dev team to implement the right product, aligned with business flow and tech limitations.
• Handle comprehensive testing across API, Web, and Mobile platforms, with a heavy focus on mobile application testing (iOS and Android).
• Work closely with the CS (Customer Support) team to investigate, reproduce, and address real user problems.

Requirements:

• A bachelor's degree in a related field is preferred (Computer Science, Informatics Engineering, Informatics Management, etc.).
• 3- 5 years of experience as a Quality Assurance Engineer.
• Fluent in English (spoken & written), as you will be collaborating with a global team.
• In-depth understanding of industry standards, QA methodologies, and company policies.
• Strong analytical and problem-solving skills, with the ability to assess risk and prioritize testing efforts accordingly.
• Exceptional attention to detail, with a systematic approach to identifying and documenting issues.
• Meticulous and diligent, with a high standard for documentation and process consistency.
• Strong customer focus with the ability to advocate for quality from the user's perspective.
• Hands-on experience with test automation frameworks (Playwright, Appium, Selenium, or Katalon) and proficient in using Git for version control, branching strategies, and maintaining automation codebases in a collaborative development environment.
• Deep working knowledge of software products, services, and the end-to-end delivery lifecycle.
• A collaborative team player who can work effectively with minimal supervision and is comfortable leading quality initiatives across cross-functional teams.
• Experienced in leveraging AI-assisted tools and workflows throughout the QA lifecycle — from test case generation and test data synthesis to defect triage and coverage gap analysis — with the judgment to critically review, refine, and integrate AI outputs into a structured, repeatable testing process.