Performance & Scalability Lead – Central Quality Team

As a Performance Lead in our Centralized Quality Team you will be the architect of our performance testing ecosystem. You wont just run tests; you will build the infrastructure and define the AI-driven methodologies that allow our R&D groups to deliver the worlds most secure Identity Security platform at massive scale. This is a high-impact role where you will bridge the gap between traditional performance engineering and the future of AI-augmented quality.

The Technical Toolkit & AI Integration

In this role you will lead the adoption and testing of performance stack:

• Hand zone development using falling tools : LoadRunnerLocust and JMeter.

• AI-Driven Testing: hand zone development GenAI (Cursor Windsurf and Claude code)

• Observability & AI-Ops: Leveraging tools like Dynatrace New Relic or Datadog 

• Cloud Native: Operating at scale within AWS and Kubernetes (EKS) utilizing chaos engineering to validate system resiliency.

What will you do

• Lead the Vision: Drive the cross-company performance strategy shifting from reactive testing to proactive Performance Engineering.

• Build the Performance Hub: Develop centralized reusable performance testing assets and AI-powered dashboards for use by all product teams.

• Smart Analysis: Use AI to analyze vast amounts of telemetry data providing actionable insights into architectural weaknesses and cost-optimization opportunities in the cloud.

• Collaborate: Partner with R&D and Product Leads to ensure performance is in our CI/CD pipelines.

• Lead performance aware SDLC process set KPIs and work with R&D and Product to ensure new features meet performance demands

• Help bring customers requirements into testing cycles of new features and regression suits

Qualifications :

• Proven Leadership: 6 years in Performance Engineering with experience leading complex technical projects or teams.

• Deep Technical Roots: Expert knowledge of performance testing tools.

• Coding Proficiency: Strong hands-on experience in Java Python or Go.

• AI Curiosity: Experience (or deep interest) in applying AI/LLMs to automate testing workflows or analyze performance metrics.

• Cloud Expertise: Extensive experience with AWS infrastructure and Kubernetes.

• RCA: AI-driven Root Cause Analysis (RCA) to pinpoint bottlenecks in complex microservices architectures.

• Communication: Ability to explain complex performance data to stakeholders and influence R&D roadmaps.

Bonus Points

• Experience with AIOps tools and predictive monitoring.

• Active contribution to the performance engineering or AI community (blogs open source meetups).

#LI-Hybrid

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Remote Work :

No

Employment Type :

Full-time