• 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.

• 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 • Design, develop, and evolve scalable backend performance frameworks • Implement and manage production traffic replay strategies • Own and drive performance, load, stress, endurance, scalability, and resilience testing • Validate real-time communication systems, event-driven architectures, and high-throughput distributed systems • Integrate performance testing into CI/CD pipelines • Leverage observability tooling to monitor, analyse, and troubleshoot system performance • Establish performance benchmarks, SLAs, SLOs, and error budgets • Collaborate with architecture and data teams to identify bottlenecks • Analyse and optimise performance across legacy monolith systems and modern microservices architectures

• Define and lead the performance engineering strategy across high-availability, cloud-native telecom platforms • Lead, train, and coordinate a distributed guild of SDETs • Design, develop, and evolve scalable backend performance frameworks using k6, Python, Java, or TypeScript • Implement and manage production traffic replay strategies • Own and drive performance, load, stress, endurance, scalability, and resilience testing • Validate real-time communication systems, event-driven architectures (Kafka, RabbitMQ) • Integrate performance testing into CI/CD pipelines • Leverage observability tooling (Grafana, CloudWatch, Coralogix, VictoriaMetrics, PMM) • Establish performance benchmarks, SLAs, SLOs, and error budgets • Collaborate with architecture and data teams to identify bottlenecks • Analyse and optimise performance across both legacy monolith systems and modern microservices architectures

• Define and lead the performance engineering strategy across high-availability, cloud-native telecom platforms • Lead, train, and coordinate a distributed guild of SDETs • Design, develop, and evolve scalable backend performance frameworks using k6, Python, Java, or TypeScript • Implement and manage production traffic replay strategies • Own and drive performance, load, stress, endurance, scalability, and resilience testing • Validate real-time communication systems, event-driven architectures, and high-throughput distributed systems • Integrate performance testing into CI/CD pipelines • Leverage observability tooling to monitor, analyse, and troubleshoot system performance • Establish performance benchmarks, SLAs, SLOs, and error budgets • Collaborate with architecture and data teams to identify bottlenecks • Analyse and optimise performance across both legacy monolith systems and modern microservices architectures