Lead QA Engineer - Performance

About the Role

We are seeking a Lead Performance Automation Engineer to define and drive performance engineering strategy, tooling, and practices across large-scale, distributed, cloud-native platforms.

This is a technical leadership role responsible for ensuring systems are:

• Scalable
• Reliable
• Resilient under load
• Optimised for performance and cost efficiency

You will lead performance testing and engineering initiatives across multiple teams, embedding non-functional quality into every stage of the software lifecycle.

Key Responsibilities

1. Performance Engineering Strategy & Leadership

• Define and own the organisation-wide performance testing and engineering strategy.
• Establish standards for:
  • Performance testing approaches
  • Workload modelling
  • Capacity planning
• Introduce and scale performance engineering practices across multiple delivery teams.
• Provide technical leadership and mentoring to QA and engineering teams on performance best practices.
• Align performance goals with business SLAs, SLOs, and user experience expectations.

2. Performance Test Architecture & Automation

• Design and implement scalable performance test frameworks and automation pipelines.
• Lead adoption of tools such as:
  • Gatling, JMeter, k6, Locust or similar
• Build reusable solutions for:
  • Load testing
  • Stress testing
  • Spike testing
  • Soak testing
• Integrate performance testing into CI/CD pipelines for continuous validation.
• Ensure performance tests are repeatable, reliable, and production-representative.

3. Workload Modelling & Test Design

• Define realistic user workload models based on production data and usage patterns.
• Design performance test scenarios reflecting:
  • Peak load
  • Concurrent users
  • Throughput and latency requirements
• Apply risk-based prioritisation for performance testing.
• Ensure coverage across:
  • APIs
  • Microservices
  • Data pipelines
  • Event-driven systems

4. Backend, API & Distributed System Performance

• Lead performance validation for:
  • Microservices architectures
  • Event-driven systems (Kafka)
  • High-throughput APIs
• Analyse latency, throughput, error rates, and bottlenecks across distributed systems.
• Validate system behaviour under failure conditions and degraded environments.