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Lead QA Engineer - Performance
akersystems • Remote/Home Based, UK
No Relocation
Posted: June 16, 2026
Job Description
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:
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
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.
- Drive continuous optimisation initiatives based on performance data.
- Align performance metrics with business outcomes and user experience.
Technology Environment
Performance Testing Tools
- Gatling, k6, JMeter, Locust (or similar)
Languages
- Java, Kotlin, Python, or JavaScript
Architecture
- Microservices, Event-driven systems, Kafka
Cloud & Infrastructure
- AWS, Kubernetes, Docker, Terraform
CI/CD
- GitHub Actions, GitLab CI, Jenkins
Observability
- Prometheus, Grafana, Datadog, ELK, Distributed Tracing
What We’re Looking For
Essential
- Proven experience as a Performance Test Lead / Performance Engineer / SDET
- Strong experience defining performance testing strategies and frameworks
- Hands-on expertise with modern performance testing tools
- Deep understanding of:
- Scalability and distributed system performance
- Cloud-native architectures
- Experience integrating performance testing into CI/CD pipelines
- Strong skills in:
- Performance analysis and bottleneck identification
- Root cause analysis and system optimisation
- Experience defining and tracking performance SLAs/SLOs and KPIs
- Ability to lead and influence cross-team quality improvements
Desirable
- Experience in high-scale or regulated environments
- Exposure to:
- Chaos engineering
- Resilience and fault injection testing
- Experience with capacity planning and cost optimisation
- Knowledge of security-performance interactions (e.g., encryption overhead)
Personal Attributes
- Strong systems thinking and analytical mindset
- Ability to translate performance data into practical engineering improvements
- Influential leader across engineering and product teams
- Proactive and outcome-driven
- Strong communication and stakeholder management skills
Additional Content
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:
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
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.
- Drive continuous optimisation initiatives based on performance data.
- Align performance metrics with business outcomes and user experience.
Technology Environment
Performance Testing Tools
- Gatling, k6, JMeter, Locust (or similar)
Languages
- Java, Kotlin, Python, or JavaScript
Architecture
- Microservices, Event-driven systems, Kafka
Cloud & Infrastructure
- AWS, Kubernetes, Docker, Terraform
CI/CD
- GitHub Actions, GitLab CI, Jenkins
Observability
- Prometheus, Grafana, Datadog, ELK, Distributed Tracing
What We’re Looking For
Essential
- Proven experience as a Performance Test Lead / Performance Engineer / SDET
- Strong experience defining performance testing strategies and frameworks
- Hands-on expertise with modern performance testing tools
- Deep understanding of:
- Scalability and distributed system performance
- Cloud-native architectures
- Experience integrating performance testing into CI/CD pipelines
- Strong skills in:
- Performance analysis and bottleneck identification
- Root cause analysis and system optimisation
- Experience defining and tracking performance SLAs/SLOs and KPIs
- Ability to lead and influence cross-team quality improvements
Desirable
- Experience in high-scale or regulated environments
- Exposure to:
- Chaos engineering
- Resilience and fault injection testing
- Experience with capacity planning and cost optimisation
- Knowledge of security-performance interactions (e.g., encryption overhead)
Personal Attributes
- Strong systems thinking and analytical mindset
- Ability to translate performance data into practical engineering improvements
- Influential leader across engineering and product teams
- Proactive and outcome-driven
- Strong communication and stakeholder management skills