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