About the role

The Performance QE team is responsible for the system level performance of a wide range of Netskope products. Expose performance issues not manifested and/or not existed on component level. We are seeking a highly skilled and motivated Performance Test Engineer to join us. The ideal candidate will have deep expertise in performance testing methodologies tools and analysis to identify and resolve critical bottlenecks before they impact our customers.

Job Summary / Whats in it for you

Your core mission is to guarantee the capacity reliability and low latency of our applications maintaining high availability at massive scale. You will thrive here if you are dedicated to partnering with teams to ship high-quality software that is fast and scalable. You will approach QE by consistently balancing customer needs with a holistic view of the entire system architecture.

Key Responsibilities / What you will be doing

• Design and Strategy: Develop comprehensive performance test strategies and plans (including load stress volume and endurance tests) by analyzing non-functional requirements and system architecture.
• Scripting and Execution: Design develop and maintain robust reusable test scripts using industry-standard tools for various protocols (e.g. HTTP Web Services APIs FTP/SFTP).
• Environment Management: Collaborate with DevOps and infrastructure teams to configure and maintain performance test environments that accurately replicate production conditions.
• Infrastructure Analysis: Monitor and analyze resource utilization (CPU memory network I/O) at the container (Docker) and orchestration (Kubernetes Pod/Node) level during load tests.
• Load Generation: Design and deploy containerized load generators (JMeter k6 Locust containers) directly onto the Kubernetes cluster to simulate massive scale with minimal overhead.
• Capacity Planning: Provide data and insights to support Kubernetes Horizontal Pod Autoscaler (HPA) and cluster autoscaler configuration and validation.
• Monitoring and Analysis: Execute performance tests and conduct deep-dive analysis of results leveraging monitoring tools to identify system bottlenecks in application code database performance network latency and infrastructure (CPU memory I/O).
• Reporting and Collaboration: Document clear concise reports summarizing test results metrics and actionable recommendations. Communicate findings to engineering and product stakeholders.
• Optimization: Work closely with software developers and architects to provide recommendations for performance tuning and code optimization.
• Process Improvement: Stay current with industry trends tools and best practices in performance engineering and contribute to the continuous improvement of the testing framework and processes.

Required Qualifications / Required skills and experience

• Experience: Minimum 12 years of experience in Performance Testing/Engineering roles with scalable cloud native multi-tenant architectures
• Tool Proficiency: Proven expertise with at least one major performance testing tool (e.g. JMeter Locust LoadRunner Gatling k6 BlazeMeter).
• Scripting/Coding: Strong proficiency in at least one scripting or programming language (e.g. Java Python Golang JavaScript) for test script development and customization.
• Containerization: Hands-on experience with Docker for containerizing applications and understanding how container resource limits affect application performance.
• Orchestration: Experience working with Kubernetes (K8s) including familiarity with concepts like Pods Deployments Services Ingress and the ability to view/analyze logs and metrics from a cluster.
• System Knowledge: Solid understanding of system architecture components including web servers application servers databases and load balancers.
• Monitoring/AI Tools: Experience with modern APM/Monitoring tools that incorporate AI/ML capabilities (e.g. Dynatrace New Relic Prometheus Grafana DataDog or custom AI solutions) for in-depth analysis.
• Database: Working knowledge of SQL/NoSQL databases and the ability to analyze and optimize database query performance.
• Data Analysis: Strong analytical skills with the ability to interpret large datasets and AI-generated outputs (e.g. log traces metrics) to pinpoint root causes.
• Foundational Knowledge (Preferred): Basic understanding of Machine Learning concepts (e.g. supervised learning predictive modeling) and their application in performance analysis is a significant advantage.

Preferred Skills

• Experience with cloud platforms (e.g. AWS Azure GCP) and testing applications deployed in a cloud-native environment.
• Familiarity with Continuous Integration/Continuous Delivery (CI/CD) pipelines and integrating performance tests into the release process.
• Experience with service virtualization or mocking tools.
• Knowledge of network protocols L2/L3 networking concepts routing protocols VPN IPSec and WAN architecture and troubleshooting
• Translate machine-generated data and patterns (from AI/ML models) into clear actionable performance tuning recommendations for the development and architecture teams.
• Explore and integrate AI tools for automating repetitive tasks such as test script maintenance (self-healing) realistic test data generation and intelligent workload pattern creation.
• Stay current with emerging AI/ML applications in performance testing and champion the adoption of new tools and methodologies to drive efficiency and accuracy.

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