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