Lead QA Engineer - Performance

Aperture Cloud was founded in 2017 by a team of experienced technology professionals who recognised an opportunity to provide highly secure enterprise data platforms to large organisations. We build and operate ground-breaking, ultra-secure, high performance, cloud-based data infrastructure for the enterprise. Our proprietary technology solutions drive performance and reduce costs while helping our clients to improve the management and sharing of data across their organisations. In 2024, Aperture Cloud won the Breakthrough Culture Awards highlighting growth companies putting culture first. In 2020 Aperture Cloud was recognised as a ‘One to Watch’ on the Sunday Times Tech Track. The Company was also recognised at the Thames Valley Tech Awards 2020; winning the Thames Valley Tech Company of the year, the Emerging Tech Company and High Growth Tech Business categories. We encourage people of all different backgrounds and identities to apply. We are committed to maintaining an inclusive, and supportive place for you to do your very best work. 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. Ensure horizontal scalability and resilience strategies are tested. 5. Cloud, Infrastructure & Scalability Testing Validate performance across: AWS cloud environments Containerised platforms (Docker, Kubernetes) Conduct capacity planning and infrastructure benchmarking. Ensure systems scale efficiently using: Auto-scaling Load balancing Distributed architectures Evaluate performance of Infrastructure as Code (Terraform) deployments. 6. Observability, Analysis & Bottleneck Resolution Use observability tools to analyse system performance, including: Metrics (Prometheus, Datadog) Logs (ELK) Traces (distributed tracing tools) Identify and diagnose: CPU, memory, I/O bottlenecks Network latency issues Database performance constraints Collaborate with engineering teams to optimise system performance and architecture. 7. Non-Functional Quality Governance Define and enforce performance SLAs, SLOs, and acceptance criteria. Establish quality gates for performance within CI/CD pipelines. Ensure performance requirements are validated before production release. Drive adoption of performance testing standards across teams. Support audit, compliance, and regulatory expectations in performance-critical systems. 8. Production Performance & Continuous Improvement Analyse real production performance data to refine testing strategies. Lead performance-related incident investigations and RCA activities. Establish feedback loops between production observability and test environments. Drive improvements in: System responsiveness Stability under load Operational resilience 9. Metrics, Reporting & Optimisation Define and track performance KPIs, including: Response times Throughput Error rates Resource utilisation Build performance dashboards and reporting frameworks. Driv