Lead Performance Engineer (SDET)

Vonage Applications

Our Applications business unit focuses on delivering a suite of advanced communication tools designed to enhance customer engagement. From cloud contact canters to feature-rich unified communications solutions, our Applications team is dedicated to providing businesses with the tools they need to streamline operations and improve customer experiences.

Why this role matters

Vonage operates high-availability, cloud-native telecom platforms that power real-time communications for global enterprises. As a Lead Performance Engineer, you will own the end-to-end performance engineering strategy, ensuring our systems can scale, endure, and recover under production-like conditions. Your work directly protects the reliability and quality of experience that our customers depend on every day.

Your key responsibilities

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 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 and modern microservices architectures.

What you'll bring

Proven experience mentoring and coordinating testing efforts across large, distributed engineering teams, including 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.

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.

Experience we consider a plus:

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.

Hands-on experience with chaos engineering tools such as AWS Fault Injection Simulator.

#LI-CK1