Qa Lead

Job description

Role - QA Lead Experience - 8 - 10 Yrs **Location - Pune** **Notice Period - Immediate Joiners** **Project Details** **Project: Enterprise Platform Quality Engineering & Reliability Assurance** The project focuses on ensuring quality, scalability, performance, and reliability of a distributed **microservices-based platform** through advanced test automation, performance engineering, observability, and AI-assisted quality practices. The QA team works closely with Engineering, DevOps, and SRE teams to validate complex customer workflows, event-driven architectures, data pipelines, and production environments. The engagement emphasizes high-quality releases, proactive defect prevention, root cause analysis, and continuous improvements in platform reliability through automation, monitoring, and performance validation. **Roles & Responsibilities:** **1. Test Automation Strategy & Framework Development** Designed and maintained scalable automation frameworks for **API, integration, and end-to-end testing** using Selenium, Playwright, Postman, RestAssured, PyTest, and TestNG, improving regression coverage across business-critical workflows. **2. Microservices & System-Level Validation** Developed comprehensive regression suites to validate **event-driven architectures, asynchronous processing, data pipelines, and cross-service customer journeys**, ensuring seamless functionality across distributed systems. **3. CI/CD Quality Enablement** Integrated automated test suites **Jenkins, GitHub Actions, and GitLab CI pipelines**, enabling continuous testing, faster feedback cycles, and high-confidence production releases. **4. Performance Engineering & Scalability Testing** Designed and executed **load, stress, endurance, and performance testing** using JMeter and K6 to identify bottlenecks, validate system scalability, and ensure platform stability under peak workloads. **5. Observability, Monitoring & Production Diagnostics** Leveraged **Datadog and Splunk** to monitor application health, investigate production incidents, perform root cause analysis, reproduce defects, and identify quality gaps based on incident trends and operational insights. **6. AI-Driven Quality Engineering & Continuous Improvement** Utilized AI-assisted tools for **test case generation, automation development, defect clustering, log analysis, anomaly detection, and test coverage optimization**, driving higher testing efficiency, improved defect detection, and enhanced release quality.