Responsibilities
- Designing comprehensive performance testing strategies, leading initiatives, and collaborating with cross-functional teams to ensure system reliability, scalability, and responsiveness across applications
- Conduct thorough performance assessments, including load testing, stress testing, and capacity planning, to identify system bottlenecks and areas for improvement
- Work closely with development and operations teams to Identifying key performance indicators (KPIs) and establishing benchmarks, monitoring solutions, and dashboards that provide real-time insights into system performance
- Architect and implement scalable testing frameworks for performance, and data validation, focusing on AI and Generative AI applications
- Lead the troubleshooting and resolution of complex performance-related issues in QA, Staging, Pre-production and/or Production environments
- Provide guidance and mentorship to junior QA engineers, fostering a culture of quality and continuous learning
- Utilize industry-standard performance testing tools (e.g., JMeter, LoadRunner, Gatling) to simulate real-world scenarios and measure system performance, staying current with emerging tools and technologies in the performance testing space
- Collaborate with development, QA, and operations teams to integrate performance testing into the continuous integration and continuous deployment (CI/CD) processes, providing guidance and support to team members on performance testing best practices
- Analyze the CPU Utilization, Memory usage, Network usage, Garbage Collection to verify the performance of the applications
- Generate performance graphs, session reports, and other related documentation required for validation and analysis
- Create comprehensive performance test documentation, including test plans, test scripts, and performance analysis reports, effectively communicating performance testing results and recommendations to technical and non-technical stakeholders
Requirements
- Bachelor’s or Master’s degree in computer science, Engineering, or a related field
- 12+ years of experience in performance testing and engineering, with a strong understanding of performance testing methodologies and tools
- Proficiency in performance testing tools such as JMeter, LoadRunner, or Gatling
- Proficiency in programming languages such as Python, Javascript, Java
- Extensive experience with cloud technologies and platforms (e.g., AWS, Azure, Google Cloud) and containerization (Docker/Kubernetes)
- Strong understanding of web technologies and application architecture
- Experience in Application Monitoring Tools and profiling tools like Datadog, Dynatrace, Grafana, AppDynamics, Splunk
- Strong experience with CI/CD pipelines and DevOps practices
- Experience in Applications like ElasticSearch, OpenSearch, Grafana, Kafka
- Hands-on experience with performance test simulations, performance analysis, performance tuning, performance monitoring in a microservices environment
- Hands- on experience in analyzing the performance results - Capture/Analyze/Interpret performance metrics from application, database, OS, and Network
- Working knowledge of SQL and cloud Databases like MongoDB, Cosmos DB, PostgreSQL
- Demonstrated ability to analyze complex systems, identify performance bottlenecks, and provide actionable insights
- Good understanding of basic DB tuning, application server tuning and common issues around performance and scalability
- Proven track record of leading performance testing teams and drive initiatives by collaborating effectively with cross-functional teams
- Strong verbal and written communication skills, with the ability to convey complex technical concepts to non-technical stakeholders
- Good understanding of computer networks and networking concepts
- Agile development experience
Nice to Have
- Experience with AI/ML frameworks (e.g., TensorFlow, PyTorch) is a plus
- Strong understanding of data validation techniques and tools
