Performance Test Lead

Responsibilities

1. Define testable NFRs, SLOs, KPIs, workloads, and acceptance criteria with product, architecture, engineering, and SRE teams. Build production-representative workload and capacity models using traffic patterns, transaction mix, arrival rates, concurrency, growth forecasts, and appropriate open or closed workload models.
2. Design and execute load, stress, spike, endurance, scalability, and capacity tests using k6, JMeter, Gatling, or equivalent tools.
3. Build correlation-heavy, data-driven scripts and operate distributed load generation at enterprise scale.
4. Use APM, metrics, logs, and traces from tools such as AppDynamics, New Relic, Grafana, Prometheus, Splunk, Elastic, or OpenTelemetry to isolate application and platform bottlenecks.
5. Test cloud and distributed platforms involving containers, Kubernetes, load balancers, databases, caches, queues, and protocols or interaction patterns beyond synchronous HTTP/REST.
6. Integrate performance tests and thresholds into CI/CD, maintain baselines, track trends, and improve test reliability and execution time.
7. Lead cross-functional triage, verify fixes, communicate risks to technical and non-technical stakeholders, and provide release sign-off recommendations.
8. Define standards, review technical work, mentor engineers, support hiring, and transition vendor-owned scripts, frameworks, documentation, and knowledge into the company.

1. Led performance engineering for multiple enterprise-scale releases with measurable improvements in latency, throughput, scalability, stability, or capacity
2. Defined NFRs and SLOs, built workload and capacity models from production demand, and justified open or closed model selection.
3. Used at least one major performance tool at real scale, including distributed load generation, complex correlation, dynamic data, and realistic pacing.
4. Diagnosed critical bottlenecks by correlating test results with APM, infrastructure, database, log, metric, and trace evidence.
5. Tested cloud-native or distributed systems and explained the performance effects of Kubernetes, autoscaling, load balancing, databases, caches, queues, networks, and dependencies.
6. Built reusable frameworks and CI/CD quality gates with reliable baselines, trend reporting, and reduced execution or diagnosis time.
7. Built or improved a performance testing practice, mentored engineers, supported hiring, governed vendors, led triage, and communicated release risk to senior stakeholders.