Sr. SQA Engineer

Job Description

Establish automation testing processes and procedures in collaboration with business teams, development, and IT Support. Build subject matter expertise in assigned systems to effectively design and execute tests. Develop automated test scripts and reusable code modules, incorporating AI-driven testing techniques where applicable. Contribute to defining testing processes and procedures with cross-functional input. Identify and analyze system defects through functional, automated regression, performance, and AI-assisted testing. Conduct test scenario reviews to ensure compliance with Asurion testing standards. Apply detailed development knowledge to deliver high-quality outcomes. Troubleshoot and resolve issues in existing test scripts with speed and accuracy. Execute automated functional tests and document results using established tools and processes. Log and track defects in accordance with defined reporting standards. Provide support for non-production (NON-PROD) environment issues, working directly with IT teams to resolve them. Collaborate with onshore and offshore teams to support code integration activities. Foster customer satisfaction and maintain high service standards across Asurion and its clients.

Qualifications And Experience

• Bachelor’s degree with at least 6-8 years of experience in software quality engineering or test engineering roles.
• Nice to have (Highly preferred): Experience with Microsoft Dynamics D365 testing.
• Must have: Minimum of 3 years of hands-on experience with automated testing tools (including Puppeteer).
• Must have: Experience with AI/ML-based testing tools or AI-assisted automation frameworks.
• Must have: Demonstrated experience building and scaling automated testing frameworks.
• Must have: Experience using Azure DevOps and JIRA.
• Proficiency in Python for test automation and scripting.
• Strong collaboration skills with experience partnering closely with engineering leadership.

Quality Metrics and Continuous Improvement

• Define, establish, and track key quality metrics and dashboards, including defect leakage, automation coverage, defect density, test execution efficiency.
• Leverage data analytics and AI-driven insights to identify trends, predict risks, and improve test effectiveness.
• Drive continuous improvement by identifying gaps, optimizing processes, and aligning quality metrics with business objectives and release readiness criteria.
• Communicate quality status, risks, and actionable insights to stakeholders through regular reporting.