Performance Test Lead

Role Mission:

• Establish scalable performance engineering practices for their business-critical applications and platforms.
• Own performance test strategy, workload modelling, execution, analysis, and release recommendations for assigned applications and platforms.
• Build in-house capability through vendor transition, technical leadership, mentoring, hiring support, and clear governance.

Responsibilities

• 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.
• Design and execute load, stress, spike, endurance, scalability, and capacity tests using k6, JMeter, Gatling, or equivalent tools.
• Build correlation-heavy, data-driven scripts and operate distributed load generation at enterprise scale.
• 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.
• Test cloud and distributed platforms involving containers, Kubernetes, load balancers, databases, caches, queues, and protocols or interaction patterns beyond synchronous HTTP/REST.
• Integrate performance tests and thresholds into CI/CD, maintain baselines, track trends, and improve test reliability and execution time.
• Lead cross-functional triage, verify fixes, communicate risks to technical and non-technical stakeholders, and provide release sign-off recommendations.
• Define standards, review technical work, mentor engineers, support hiring, and transition vendor-owned scripts, frameworks, documentation, and knowledge into the company.

What you’ll bring

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