QA Automation Lead - Performance Testing
New
IndiaFull-TimeLead
Salary not disclosed
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Job Details
- Experience
- 7–10 years of experience in software quality assurance, including at least 5 years specializing in performance, load, stress, and scalability testing
- Required Skills
- AWSPythonJMeterAPI testingCI/CDDatadogDistributed Systems
Requirements
- 7–10 years of experience in software quality assurance
- At least 5 years specializing in performance, load, stress, and scalability testing
- Strong hands-on experience designing performance testing strategies for distributed systems, web applications, and APIs
- Advanced proficiency in Python for building scalable and maintainable test automation frameworks
- Experience with performance testing tools such as Locust, JMeter, Gatling, or similar tools
- Strong understanding of performance metrics including latency, throughput, response time, error rates, and system resource utilization
- Experience working with CI/CD pipelines and implementing automated performance gates and regression detection
- Hands-on experience with AWS infrastructure, containerized environments, and modern cloud-native architectures
- Experience with observability and APM tools such as Datadog, New Relic, or Dynatrace for root cause analysis
- Strong analytical, debugging, and problem-solving skills
- Excellent communication skills
- Exposure to AI-driven testing approaches, including LLM validation, prompt testing, and AI-assisted automation (strong plus)
Responsibilities
- Define and lead the performance testing and performance engineering strategy, ensuring systems are scalable, reliable, and optimized for high-traffic usage scenarios
- Design, develop, and execute performance, load, stress, and endurance tests for web applications, APIs, and backend services, ensuring early identification of performance risks
- Analyze system behavior under load to identify bottlenecks across application code, APIs, databases, infrastructure, and third-party services
- Build and maintain performance automation frameworks using Python and tools such as Locust, integrating AI-driven approaches to generate realistic traffic models and test scenarios
- Integrate performance testing into CI/CD pipelines, establishing automated performance gates to prevent regressions from reaching production
- Collaborate with engineering, DevOps, SRE, and product teams to define performance benchmarks, SLAs, capacity planning, and scalability requirements
- Produce clear performance reports and dashboards with actionable insights, trends, and recommendations for optimization and system improvements
- Drive adoption of performance engineering best practices and promote a “shift-left” approach within the SDLC
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