Lead Performance QA Engineer

Come work at a place where innovation and teamwork come together to support the most exciting missions in the world! Performance Engineering Lead Location: Pune / Hybrid (3 days in office) Experience: 7+ Years Employment Type: Full-Time About the Role We are looking for an experienced and highly motivated Performance Engineering Lead to drive performance validation, scalability assessment, and performance optimization across a modern cloud-native platform. The ideal candidate will have strong expertise in distributed microservices architectures, Kubernetes environments, messaging systems, APIs, big data workloads, and performance troubleshooting. The role requires both hands-on technical excellence and leadership capabilities to plan, execute, analyze, and drive performance initiatives across release cycles while collaborating closely with development, architecture, operations, and customer-facing teams. Key Responsibilities Performance Strategy & Leadership • Own performance engineering activities across multiple releases and product roadmaps. • Define performance testing scope, objectives, workload models, success criteria, and capacity planning requirements. • Plan, track, and deliver performance testing activities within release timelines. • Provide regular performance status updates, risk assessments, and recommendations to stakeholders. • Mentor and guide performance engineers and establish performance engineering best practices. • Performance Testing & Engineering • Design and execute performance, load, stress, endurance, scalability, spike, and capacity tests. • Validate performance of cloud-native applications running on Kubernetes (K8s). • Build realistic workload models that simulate production-scale traffic and customer usage patterns. • Establish performance baselines and continuously improve system scalability and reliability. Microservices Performance Analysis Perform detailed root cause analysis and optimization recommendations for applications leveraging: • Kubernetes-based microservices • Kafka-based event-driven architectures • REST and GraphQL APIs • Redis caching layers • Oracle databases • Elasticsearch clusters • Spark-based batch and streaming workloads Analyze system behavior across: • CPU utilization • Memory consumption • Thread utilization • Garbage collection • Network latency • Resource contention • Database bottlenecks • Cache efficiency • Message processing delays Kafka & Messaging Performance • Design and execute load testing for Kafka-based service-to-service messaging systems. • Analyze consumer lag, throughput, partition distribution, batching efficiency, and message processing latency. • Tune Kafka producer, broker, and consumer configurations to optimize performance. • Identify bottlenecks and provide recommendations to engineering teams for scalability improvements. API Performance Engineering • Validate latency, throughput, concurrency, error rates, and service-level objectives (SLOs). • Perform end-to-end performance analysis across distributed API ecosystems. • Identify bottlenecks in service interactions and recommend optimizations. Spark Performance Optimization • Analyze Spark batch and streaming job performance. • Investigate executor utilization, partitioning strategies, shuffle operations, memory management, and query execution plans. • Recommend performance improvements to reduce execution time and infrastructure costs Performance Diagnostics & Optimization • Conduct deep-dive investigations into production and pre-production performance issues. • Collaborate with developers to identify root causes and implement fixes. • Review architecture and code changes from a scalability and performance perspective. • Recommend improvements in application design, infrastructure configuration, caching, database queries, and messaging patterns. Customer issues: • Investigate customer-reported performance and scalability issues. • Reproduce production scenarios and provide actionable recommendations. • Work with support and engineering teams to ensure timely resolution of customer-impacting performance problems. Test Data & Automation • Design and develop scalable test data generation frameworks. • Create automation utilities and performance engineering tools. • Develop scripts and utilities using Python, Java, or similar programming languages to support performance testing and analysis. Required Skills & Qualifications Experience • 7+ years of experience in Performance Testing and Performance Engineering. • Proven experience leading performance initiatives for enterprise-scale applications. • Strong understanding of distributed systems and cloud-native architectures. Technical Expertise • Kubernetes (K8s) and containerized environments • Microservices architecture • Apache Kafka • API performance testing and diagnostics • Redis • Oracle Database • Elasticsearch • Apache Spark • Linux system performance analysis Performance Testing Tools Experience with one or more: • JMeter • Gatling • LoadRunner • k6 • Locust • BlazeMeter Monitoring & Observability Hands-on experience with: • Grafana • Prometheus • Kibana • Elastic Stack • Dynatrace • AppDynamics • OpenTelemetry Programming & Automation Strong scripting/programming experience in: • Python • Java