Performance Engineering Lead

Role Description

As the Performance Engineering Lead, you will be responsible for building and owning Appspace's performance engineering capability from the ground up. This is a foundational role — you will define the strategy, select the toolchain, establish the first baselines across our product suite, and integrate performance gates directly into our CI/CD pipeline. You will serve as the technical authority on all performance decisions, ensuring that every service boundary in Appspace's platform is covered by a measurable, enforceable performance contract. As we scale our AI-driven development lifecycle (AIDLC), you will also ensure that AI-generated code meets the same performance standards as human-written code — making automation the enforcer of quality, not a bypass of it.

• Own end-to-end performance testing strategy across all Appspace product lines — Digital Signage, Space Reservation, Visitor Management, and Intranet.
• Design, build, and maintain a performance testing framework using k6 (primary) and Locust, with a baseline artifact store in Google Cloud Storage.
• Establish performance baselines for critical user journeys and service boundaries, starting with Signage as the highest-traffic product line.
• Integrate automated performance gates into the CI/CD pipeline, ensuring regressions are caught before they reach production.
• Leverage AI and AI tools throughout the entire test lifecycle—from generating test cases, optimizing test execution schedules, to autonomously monitoring test results and identifying performance degradation patterns.
• Define and enforce performance SLAs — including p95 latency targets, throughput thresholds, and error rate limits — across all product domains.
• Work closely with AI Delivery Leads in each product squad to ensure that AI-generated code touching service boundaries triggers automated load test candidates in the pipeline.
• Co-author performance test plans with domain engineers across Signage, Space+Visitor, Intranet, and Core Platform squads.
• Build and operate regression detection alerting, feeding results into the AIDLC metrics framework and engineering leadership dashboards.
• Champion performance engineering best practices across globally distributed engineering teams.
• Communicate performance posture, test coverage progress, and regression trends accurately and regularly to engineering leadership.
• Grow the function over time — mentoring a Phase 2 AI QE / Performance Engineer sourced from the internal QA retraining programme.

Qualifications

• Degree in Computer Science, Software Engineering, or equivalent.
• Minimum 7 years' experience in software engineering including at least 4 years of strong focus on performance testing, load testing, or site reliability engineering for multi-tenant, SaaS applications.
• Proven hands-on experience designing and executing load and performance test strategies at scale — not just running tools, but owning the programme.
• Strong proficiency with k6 and/or Locust for scripting and executing load tests against REST APIs and web applications.
• Experience integrating performance tests into CI/CD pipelines (e.g., GitHub Actions, Bamboo, Jenkins) as quality gates.
• Experience with Google Cloud Platform (GCP) and Kubernetes — ability to reason about infrastructure behaviour under load.
• Practical familiarity and hands-on experience using Generative AI tools (e.g., Claude, Cursor) to augment test development, scripting, or analysis workflows.
• Proficiency in Python and/or JavaScript/TypeScript for test scripting and tooling automation.
• Strong understanding of microservices architecture, API design, and distributed system performance characteristics.
• Experience with observability tooling — Grafana, Prometheus, Cloud Monitoring, or equivalent — for baseline measurement and regression detection.
• Experience with version control tools (Git) and familiarity with trunk-based development workflows.
• Strong analytical and problem-solving skills — able to diagnose performance regressions from first principles, not just tooling output.
• Excellent communication skills, both written and verbal — able to translate technical performance data into clear engineering and leadership narratives.
• Ability to work independently in a globally distributed team environment across time zones.

Requirements

• Experience working with or evaluating AI-generated code in an engineering pipeline context.
• Familiarity with chaos engineering principles and tooling (e.g., Chaos Monkey, Gremlin).
• Experience with SQL and/or NoSQL databases (e.g., MongoDB, PostgreSQL) and understanding of database performance under load.
• Experience with BrowserStack or equivalent cross-browser and cross-device performance testing.
• Background in SRE or platform engineering at a SaaS company.

Benefits

• For all our KL based team members, we offer a variety of benefits from competitive salaries, medical, dental and vision coverage, mental health resources, a 14 week maternity leave program and transport/parking allowance.
• 20 Days PTO
• Flexible work schedules
• Remote work opportunities
• Paid company holidays
• Appspace Quiet Fridays (No non-essential internal meetings scheduled)
• A casual dress work environment