Lead Performance Tester (IC Leadership Role)

Core Responsibility

• Architect and lead enterprise-grade performance testing frameworks across multiple distributed systems, APIs, SDKs, and service integrations.
• Design, execute, and interpret performance, load, soak, and stress tests that simulate real-world concurrency and traffic behaviors.
• Build reusable performance harnesses using modern tools such as JMeter, k6, Gatling, Locust, or LoadRunner Enterprise, and integrate them into CI/CD pipelines.
• Author and maintain the Performance Testing Runbook — a detailed operational guide covering environment setup, load profiles, KPIs, and performance sign-off criteria.
• Instrument systems for observability, leveraging APM and monitoring tools (Datadog, New Relic, Grafana, Prometheus, ELK, OpenTelemetry) to trace and correlate test results with backend telemetry.
• Perform deep-dive bottleneck analysis at every layer of the stack — network, API, cache, database, and compute — identifying contention, saturation points, and tuning opportunities.
• Work directly with backend, DevOps, and SRE teams to benchmark system capacity, analyze regressions, and validate optimization hypotheses.
• Integrate performance metrics into CI/CD, enforcing data-driven go/no-go gates and providing clear visual reports on trend lines and performance drift.
• Mentor and influence QA, automation, and infrastructure engineers in the art and science of performance engineering.

Required Technical Skills & Expertise

• 8+ years of hands-on experience in performance, load, and scalability testing for large-scale or enterprise-grade systems.
• Advanced proficiency with multiple performance testing tools, including:
• JMeter, k6, Gatling, LoadRunner Enterprise, Locust, NeoLoad, or BlazeMeter.
• Expertise in test scripting and automation using Python, JavaScript/TypeScript, or Groovy.
• Deep understanding of distributed architectures — microservices, APIs, message queues, and async processing.
• Proficiency with profiling and analysis tools (YourKit, JProfiler, VisualVM, Chrome DevTools, perf, flame graphs).
• Proven ability to correlate and interpret metrics from APM platforms (Datadog, New Relic, Dynatrace, AppDynamics) and system monitors (Grafana, Prometheus, CloudWatch).
• Solid grasp of network and infrastructure fundamentals: TCP/IP, HTTP/2, CDN behavior, caching, load balancing, and service mesh patterns.
• Experience defining SLAs, SLOs, and performance baselines, and embedding them into pipelines as measurable quality gates.
• Strong analytical mindset — able to turn high-volume telemetry data into clear narratives, actionable insights, and capacity forecasts.
• Git proficiency and familiarity with CI/CD pipelines (Jenkins, GitHub Actions, GitLab CI, or CircleCI).

Preferred / Bonus Experience

• Familiarity with containerized and cloud-native performance testing (Kubernetes, Docker, AWS Fargate, GCP GKE).
• Experience with chaos engineering or resilience testing frameworks (Gremlin, Litmus, ChaosMesh).
• Understanding of database performance tuning and query profiling (PostgreSQL, MySQL, Redis, MongoDB).
• Knowledge of API gateway performance behavior (Kong, NGINX, AWS API Gateway).
• Prior experience authoring or maintaining enterprise performance frameworks or internal testing platforms.
• Contributions to open-source or internal performance engineering playbooks, documentation, or frameworks.

What Success Looks Like

• A Performance Testing Runbook that becomes the canonical guide for all future load and scalability testing at Xsolla.
• Continuous, automated performance baselines integrated into the build/release pipelines.
• Systemic visibility into performance regressions and bottlenecks across services.
• A quantifiable performance improvement culture, where every release is measured and optimized, not guessed.
• Clear, data-backed recommendations that drive architectural improvements and product reliability.