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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:
