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**

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• *Location:** Pune / Hybrid (3 days in office)

• *Experience:** 7\+ Years

• *Employment Type:** Full\-Time

• *About the Role**
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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