Project Description A cloud platform team develops a cloud-based service that helps organizations manage software products and services with a focus on adopting AI-powered development tools. The platform provides centralized management, usage-based billing, and visibility into AI consumption patterns.
We are currently looking for a skilled Performance QA Engineer to join the team and elevate system reliability. In this role, you will be responsible for ensuring the scalability and stability of distributed systems through rigorous performance testing and analysis. Required Qualifications * 5+ years of experience in performance testing or engineering. * Strong hands-on experience with at least one major performance testing tool: K6, JMeter, Gatling, Locust, or LoadRunner (code-based tools preferred; Git knowledge is a must). * Solid programming or scripting skills in Java, Python, JavaScript/TypeScript, or Go. * Strong understanding of distributed systems, API performance, databases, caching, and networking fundamentals. * Proven ability to analyze performance metrics, identify bottlenecks, and recommend improvements. * Experience testing high-traffic or mission-critical applications. * Strong analytical skills and ability to communicate technical findings clearly. * Proficiency in English (written and verbal).
Nice to Have * Experience with observability platforms (Prometheus/Grafana, Elastic). * Experience with containerized and cloud-native environments (Docker, Kubernetes, AWS/GCP/Azure). * Familiarity with event-driven systems, streaming platforms (Kafka), and microservice architectures. * Knowledge of CI/CD automation (TeamCity, GitHub Actions, etc.).
Candidate Responsibilities As part of the team, you will: * Design, develop, and maintain automated performance test suites for complex distributed systems. * Identify system bottlenecks, conduct root-cause analysis, and partner with engineering teams to implement remediation strategies. * Generate high-quality performance reports with actionable recommendations and communicate findings to both technical and non-technical stakeholders. * Work closely with cross-functional development teams to identify performance-critical components and define SLAs, performance criteria, and workload models. * Design and enhance frameworks for performance test execution, test results collection, analysis, visualization, and reporting. * Integrate performance testing into CI/CD pipelines.
