Lead Performance Tester (SDET)

Job description

What You Will Do

• Define and lead the performance engineering strategy across high-availability, cloud-native telecom platforms.
• Lead, train, and coordinate a distributed guild of SDETs across multiple feature teams, guiding them to build, maintain, and execute performance scripts (k6) within their fractional sprint capacity.
• Design, develop, and evolve scalable backend performance frameworks using k6, Python, Java, or TypeScript, focusing entirely on APIs, databases, and infrastructure.
• Implement and manage production traffic replay strategies (e.g., GoReplay) and complex multi-tenant test data generation to accurately recreate realistic production contention and load profiles.
• Own and drive performance, load, stress, endurance, scalability, and resilience testing, including chaos engineering practices (e.g., using AWS Fault Injection Simulator).
• Validate real-time communication systems, event-driven architectures (Kafka, RabbitMQ), and high-throughput distributed systems under production-like conditions.
• Integrate performance testing into CI/CD pipelines, enabling continuous performance validation, shift-left practices, and automated PR blocking for latency degradation.
• Leverage observability tooling (Grafana, CloudWatch, Coralogix, VictoriaMetrics, PMM) to monitor, analyse, and troubleshoot system performance.
• Establish performance benchmarks, SLAs, SLOs, and error budgets aligned with business and system requirements.
• Collaborate with architecture and data teams to identify bottlenecks, validate database architectures (e.g., MySQL, Tungsten), and present evidence-based investment proposals.
• Analyse and optimise performance across both legacy monolith systems (requiring integrated load testing) and modern microservices architectures.

What You Will Bring

• Proven experience mentoring and coordinating testing efforts across large, distributed engineering teams (e.g., conducting code reviews, guiding SDETs through framework adoption, and managing cross-team test schedules).
• Strong hands-on experience with AWS cloud services (EC2, ECS, RDS, API Gateway) with a focus on scalability and performance optimisation.
• Proven expertise in performance testing tools (specifically k6, along with JMeter or Gatling), including test design, execution, and analysis at scale.
• Hands-on experience with production traffic shadowing or replay tools (such as GoReplay).
• Deep experience testing distributed systems, message brokers (Kafka, RabbitMQ), microservices architectures, and high-volume, low-latency APIs.
• Solid backend programming and scripting skills in Python, Java, or TypeScript to build custom performance frameworks and tooling.
• Strong understanding of system design, concurrency, throughput, latency, and fault tolerance principles.
• Experience implementing performance testing within CI/CD pipelines (Jenkins, GitHub Actions) and enabling continuous performance feedback loops.
• Hands-on experience with observability and monitoring tools (Grafana, Coralogix, PMM) for performance analysis and diagnostics.
• Experience working within Agile environments and translating performance insights into actionable engineering outcomes.

Required

• Demonstrable hands-on experience in backend performance engineering and automation using Python, Java, or TypeScript.
• Proven track record of performance, scalability, and resilience testing in AWS-based cloud-native systems.
• Strong experience designing large-scale performance test strategies and leading QA/SDET teams to execute them.
• Deep understanding of API performance testing, integration testing, and system-level non-functional validation.
• Solid grasp of system architecture, distributed systems behaviour, and non-functional testing methodologies.
• Experience with observability, monitoring, and log analysis tools.
• Familiarity with RESTful APIs and event-driven systems.

Application Requirements

• Updated CV clearly demonstrating performance engineering leadership, cross-team coordination, AWS expertise, and backend automation frameworks.
• Summary of performance testing frameworks, tools (e.g., k6, GoReplay), and CI/CD methodologies implemented.
• Detailed examples of performance testing engagements (scale, environments, bottlenecks identified, business impact, improvements delivered).
• Evidence of driving performance strategy, mentoring QA/SDET teams, and influencing architecture.

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