Performance Tester
Overview We’re seeking a hands-on Performance Test Engineer to design and execute the end-to-end performance strategy for an ad-serving platform (Akka-based Java microservices) targeting <50ms response times and 4–5 million concurrent requests/users. You’ll build the test harness, model real-world traffic, execute large-scale distributed load tests, and turn findings into actionable tuning guidance. Responsibilities:
Own the performance test strategy & plan (load, stress, spike, soak, scalability, failover).
Model traffic for ad-supported streaming (burstiness, fan-out, cache hit/miss, cold-start, geo distribution, p95/p99/p999).
Build automated load frameworks & scripts (preferably Locust/Python; JMeter where appropriate). Parameterize data, correlations, and think-time.
Orchestrate distributed load generation (cloud workers, containerized runners) to simulate 4–5M concurrent at scale.
Integrate with observability/APM (metrics, logs, traces) to correlate system bottlenecks across app, JVM/GC, Akka dispatchers, network, caches, and databases.
Produce capacity models & SLAs/SLOs dashboards; run performance gates in CI/CD.
Partner with DevOps & developers to recommend tuning (thread pools, connection pools, GC, autoscaling, cache strategies, DB indexes/queries).
Document test design, scenarios, results, and clear remediation plans.
Technology Proficiency Needed:
Load tools: Locust (Python), JMeter; (nice to have: k6, Gatling).
Scripting & automation: Python (core), Bash; infra spin-up via Terraform/Docker/Kubernetes for load farms.
Metrics/Tracing: CloudWatch, OpenTelemetry, Prometheus/Grafana; log analysis pipelines.
Familiarity with Java service behaviors (Maven/Gradle pipelines, JVM/GC basics); Akka concepts are a plus.
What makes you a great fit
3–5+ years in performance engineering for large-scale, low-latency distributed systems; streaming/ad-tech exposure is a plus.
Demonstrated success hitting strict SLAs (p95/p99 latency) under millions of users/RPS.
Strong Python and test-automation skills; ability to build maintainable, reusable test frameworks.
Experience designing realistic workload models, synthetic data generation, and distributed load execution in cloud.
Analytical, communicates crisply with stakeholders, converts data into prioritized recommendations.
Logistics
Location: Remote (prefer India candidates)
Schedule: Must join US morning calls (Eastern Time) as needed.
Start: 1–3 weeks from offer.
Term: Through end of January (likely extension).
Performance Tester
Overview We’re seeking a hands-on Performance Test Engineer to design and execute the end-to-end performance strategy for an ad-serving platform (Akka-based Java microservices) targeting <50ms response times and 4–5 million concurrent requests/users. You’ll build the test harness, model real-world traffic, execute large-scale distributed load tests, and turn findings into actionable tuning guidance. Responsibilities:
Own the performance test strategy & plan (load, stress, spike, soak, scalability, failover).
Model traffic for ad-supported streaming (burstiness, fan-out, cache hit/miss, cold-start, geo distribution, p95/p99/p999).
Build automated load frameworks & scripts (preferably Locust/Python; JMeter where appropriate). Parameterize data, correlations, and think-time.
Orchestrate distributed load generation (cloud workers, containerized runners) to simulate 4–5M concurrent at scale.
Integrate with observability/APM (metrics, logs, traces) to correlate system bottlenecks across app, JVM/GC, Akka dispatchers, network, caches, and databases.
Produce capacity models & SLAs/SLOs dashboards; run performance gates in CI/CD.
Partner with DevOps & developers to recommend tuning (thread pools, connection pools, GC, autoscaling, cache strategies, DB indexes/queries).
Document test design, scenarios, results, and clear remediation plans.
Technology Proficiency Needed:
Load tools: Locust (Python), JMeter; (nice to have: k6, Gatling).
Scripting & automation: Python (core), Bash; infra spin-up via Terraform/Docker/Kubernetes for load farms.
Metrics/Tracing: CloudWatch, OpenTelemetry, Prometheus/Grafana; log analysis pipelines.
Familiarity with Java service behaviors (Maven/Gradle pipelines, JVM/GC basics); Akka concepts are a plus.
What makes you a great fit
3–5+ years in performance engineering for large-scale, low-latency distributed systems; streaming/ad-tech exposure is a plus.
Demonstrated success hitting strict SLAs (p95/p99 latency) under millions of users/RPS.
Strong Python and test-automation skills; ability to build maintainable, reusable test frameworks.
Experience designing realistic workload models, synthetic data generation, and distributed load execution in cloud.
Analytical, communicates crisply with stakeholders, converts data into prioritized recommendations.
Logistics
Location: Remote (prefer India candidates)
Schedule: Must join US morning calls (Eastern Time) as needed.
Start: 1–3 weeks from offer.
Term: Through end of January (likely extension).