Lead Performance QA Engineer

ABOUT YOU   We’re seeking a Lead Performance Tester to define, execute, and institutionalize performance testing across this landscape. This is a hands-on, technically deep individual contributor leadership role. You’ll architect and drive performance testing strategies, craft sophisticated test harnesses, and author the Performance Testing Runbook that sets the gold standard for all future testing efforts.   If you thrive in environments where every number tells a story — latency, throughput, resource contention, and scaling curves — this is your stage.   ABOUT US   Xsolla is a global commerce company with robust tools and services to help developers solve the inherent challenges of the video game industry. From indie to AAA, companies partner with Xsolla to help them fund, distribute, market, and monetize their games. Grounded in the belief in the future of video games, Xsolla is resolute in the mission to bring opportunities together, and continually make new resources available to creators. Headquartered and incorporated in Los Angeles, California, Xsolla operates as the merchant of record and has helped over 1,500+ game developers to reach more players and grow their businesses around the world. With more paths to profits and ways to win, developers have all the things needed to enjoy the game. For more information, visit xsolla.com.

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, Open Telemetry) 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.

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.

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.