Principal QA Engineer: AI-Driven Testing & Performance Lead

BTSE

Hong Kong

On-site

HKD 1,800,000 - 2,400,000

Full time

3 days ago

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Job summary

BTSE is seeking a Principal QA Engineer in Hong Kong to own automated and performance testing for high-volume trading platforms. You will design and drive test automation across API, UI, integration, and regression, while leveraging AI tools to accelerate testing and triage.

You will collaborate with engineering, product, and DevOps to ensure releases meet reliability targets. You will lead load, stress, and performance testing on critical areas such as order flow, matching, balances, and

Qualifications

7+ years in software QA/test engineering with a strong automation focus.
Must be fluent in Mandarin.
Hands-on automation experience in Python, JavaScript/TypeScript, or Java, including designing or substantially owning a framework.
Hands-on performance/load testing experience (e.g., k6, JMeter, Locust, Gatling).
Experience testing APIs and backend/distributed systems.
Experience with CI/CD (e.g., GitLab CI, Jenkins, GitHub Actions) in an agile environment.
Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.

Responsibilities

Own the testing lifecycle for new features and changes, including scoping, risk assessment, test depth, and release readiness.
Build and maintain automated tests covering API, UI, integration, and regression, and reduce flaky tests.
Design and run load, stress, and performance tests on high-risk areas (order flow, matching and settlement, balances, deposits/withdrawals), analyze results, and report bottlenecks to engineering.
Use AI tools to speed up test generation, analysis, and triage, and share what works with the team.
Integrate automated tests into CI/CD pipelines with reliable quality gates.
Lead testing on complex or high-severity changes and make release sign-off recommendations.
Own the test automation architecture (frameworks, test data, environments) in line with QA standards set by the Director.
Mentor QA engineers through code reviews and pairing.
Report quality metrics (defect escape rate, critical-path coverage, release stability) to the Director and stakeholders, and raise quality and release risks early, backed by data.