Lead Performance QA Engineer

Location: Pune

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