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Lead Performance QA Engineer at Qualys, Pune

Lead Performance QA Engineer at Qualys in Pune. Full-time. Apply with swipeandhired.

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