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Open nowPosted 4 days ago

Lead Performance QA Engineer

Qualys190 open roles

Where
Pune
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Your applicationOpen nowLead Performance QA EngineerQualys · Pune

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The clock on this job

Early applications get read.

13.5% of postings close within 7 days. Measured by our own scanner across the market. Qualys postings stay open a median of 50 days.

Share of postings closed within
  1. 2.3%1 day
  2. 4.6%3 days
  3. 13.5%7 days
  4. 21.8%14 days
  5. 36.6%30 days
  6. 58.9%60 days
This job: posted 4 days ago

Qualys median: 50 days open

The posting

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
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