Lead Performance QA Engineer

Performance Engineering Lead

About the Role

Performance Strategy & Leadership

Performance Testing & Engineering

Microservices Performance Analysis

Kafka & Messaging Performance

API Performance Engineering

Spark Performance Optimization

Performance Diagnostics & Optimization

Customer issues:

Test Data & Automation

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.
• 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.
• 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
• JMeter
• Gatling
• LoadRunner
• k6
• Locust
• BlazeMeter
• Monitoring & Observability
• Grafana
• Prometheus
• Kibana
• Elastic Stack
• Dynatrace
• AppDynamics
• OpenTelemetry
• Programming & Automation
• Python
• Java

Technical Expertise

Performance Testing Tools

Monitoring & Observability

Programming & Automation

Responsibilities

• 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 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
• 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.
• 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.
• 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
• 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.
• 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.
• 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