Performance Test Lead

StarHub Ltd; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

StarHub Ltd in Singapore is seeking an experienced Performance Engineer to establish scalable testing practices for business-critical applications and platforms.

You will design NFRs/SLOs, build workload models, lead cross-functional teams, and guide tooling like k6, JMeter, and APM suites while integrating tests into CI/CD.

Qualifications

• Experience defining NFRs, SLOs and KPIs for large-scale systems.

Responsibilities

• Define testable NFRs, SLOs, KPIs, workloads, and acceptance criteria with product, architecture, engineering, and SRE teams.

Skills

Performance testing

Load testing

Distributed load

CI/CD involvement

Cross-team leadership

Tools

k6

JMeter

Gatling

AppDynamics

New Relic

Grafana

Prometheus

Splunk

Elastic

OpenTelemetry

Kubernetes

Job description

Job Description

Role Mission

Establish scalable performance engineering practices for StarHub’s business-critical applications and platforms.

Accountabilities:

1. 1. Own performance test strategy, workload modelling, execution, analysis, and release recommendations for assigned applications and platforms.
2. 2. Establish NFRs, SLOs, baselines, capacity models, quality gates, engineering standards, and reusable automated test assets.
   Build in-house capability through vendor transition, technical leadership, mentoring, hiring support, and clear governance.

Responsibilities:

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

Areas of Impact:

1. 1. Scope: Business-critical web, mobile-backend, API, microservice, integration, data, and cloud-native platforms.
2. 2. Stakeholders: Quality Engineering, Engineering, Product, Architecture, DevOps/SRE, Infrastructure, Delivery, Operations, and vendors.
3. 3. Decision rights: Own performance test approach and evidence standards; define quality gates; approve test completion; recommend holding or blocking releases when agreed criteria are unmet.
4. 4. Leadership: Technically lead in-house and vendor engineers, establish practice standards, and contribute to workforce planning and hiring.

Ideal Track Record:

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