Lead Performance QA Engineer

Overview

Own performance engineering across a cloud-native platform, validating performance, scalability and reliability and leading performance initiatives across releases.

What you'll do

• 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.
• 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.
• Perform root cause analysis and optimization recommendations for Kubernetes-based microservices, Kafka-based event-driven architectures, REST and GraphQL APIs, Redis caching layers, Oracle databases, Elasticsearch clusters, and Spark-based batch and streaming workloads.
• Analyze CPU utilization, memory consumption, thread utilization, garbage collection, network latency, resource contention, database bottlenecks, cache efficiency, and message processing delays.
• 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.
• Validate latency, throughput, concurrency, error rates, and service-level objectives (SLOs) across distributed API ecosystems.
• Analyze Spark batch and streaming job performance, including executor utilization, partitioning strategies, shuffle operations, memory management, and query execution plans.
• Conduct deep-dive investigations into production and pre-production performance issues and collaborate with developers on root causes and fixes.
• Review architecture and code changes from a scalability and performance perspective.
• Investigate customer-reported performance and scalability issues, reproduce production scenarios, and work with support and engineering teams to resolve them.
• Design and develop scalable test data generation frameworks and performance engineering automation tools using Python, Java, or similar languages.

What you'll need

• 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 in Kubernetes (K8s) and containerized environments, microservices architecture, Apache Kafka, API performance testing and diagnostics, Redis, Oracle Database, Elasticsearch, Apache Spark, and Linux system performance analysis.
• Experience with one or more performance testing tools: JMeter, Gatling, LoadRunner, k6, Locust, or BlazeMeter.
• Hands-on experience with monitoring and observability tools: Grafana, Prometheus, Kibana, Elastic Stack, Dynatrace, AppDynamics, or OpenTelemetry.
• Strong scripting/programming experience in Python and Java.

Details

• Location: Pune, hybrid (3 days in office).
• Employment type: Full-time.