Senior Performance Test Engineer

Key Skills: JMeter, LoadRunner, Gatling, Python, JavaScript, Java, AWS, Azure, GCP, Docker, Kubernetes, Datadog, Dynatrace, Grafana, AppDynamics, Splunk, CI/CD, MongoDB, PostgreSQL, Cosmos DB, Kafka, Elasticsearch, TensorFlow, PyTorch, Performance Tuning, System Monitoring, Microservices, SQL, Agile, DevOps, Data Validation, REST APIs, Cloud Infrastructure, Technical Leadership, Network Analysis.

Roles & Responsibilities:

• Design and lead comprehensive performance testing strategies to ensure system reliability, scalability, and responsiveness across applications.
• Conduct load, stress, and capacity testing to identify performance bottlenecks and areas for optimization.
• Collaborate with cross-functional teams to define KPIs, establish benchmarks, and implement real-time monitoring dashboards.
• Architect and implement scalable performance testing frameworks, particularly for AI and Generative AI applications.
• Lead troubleshooting and resolution of performance issues in QA, staging, pre-production, and production environments.
• Mentor junior QA engineers, promoting quality-focused practices and continuous learning.
• Utilize tools such as JMeter, LoadRunner, or Gatling to simulate real-world performance scenarios.
• Integrate performance testing into CI/CD pipelines and ensure continuous monitoring.
• Analyze resource usage including CPU, memory, network utilization, and garbage collection behavior.
• Generate and present detailed performance reports, graphs, and test documentation to technical and non-technical stakeholders.

Experience Requirements:

• 15-20 years of experience in performance testing and engineering.
• Strong proficiency in using performance testing tools like JMeter, LoadRunner, or Gatling.
• Solid programming skills in Python, JavaScript, and Java.
• Deep experience with cloud platforms (AWS, Azure, GCP) and containerization tools like Docker and Kubernetes.
• Hands-on expertise with performance monitoring and profiling tools like Datadog, Dynatrace, AppDynamics, Grafana, and Splunk.
• Significant experience with microservices-based architectures and testing performance in distributed systems.
• Hands-on experience analyzing performance results including metrics from application, database, OS, and network layers.
• Familiarity with CI/CD pipelines and DevOps practices.
• Experience with databases such as MongoDB, Cosmos DB, and PostgreSQL.
• Exposure to tools and platforms like Kafka, Elasticsearch/OpenSearch.
• Understanding of AI/ML frameworks such as TensorFlow or PyTorch is a plus.
• Proven ability to lead performance testing teams and work effectively across functions.
• Strong experience in data validation and performance tuning techniques.

Education: M.E., B.Tech M.Tech (Dual), B.E., B.Tech, M. Tech