Senior Quality Assurance Engineer

Traveloka; Singapore; On-site; SGD 150,000 - 190,000; Full time

Job summary

Traveloka is seeking an experienced Senior SDET to join our platform team in Singapore. You will architect and maintain full-stack test frameworks, govern test data, and develop service mock infrastructure to accelerate delivery.

Responsibilities include building scalable test pipelines, improving test coverage, and leveraging AI-driven QA techniques to enhance product quality and efficiency across Web, Mobile, and backend services.

Qualifications

• 8+ years in SDET or test development roles.
• Proven track record building end-to-end test frameworks for Web/Mobile/API.
• Experience with test data governance and service virtualization.
• Familiarity with AI quality engineering concepts.

Responsibilities

• Design and develop end-to-end test frameworks for Web, Mobile and APIs.
• Lead test data governance and automate data factories.
• Design and maintain Mock services and service virtualization.
• Maintain CI/CD/CT pipelines for seamless testing integration.
• Develop dashboards for QE metrics and AI cost monitoring.

Skills

SDET

Test Frameworks

CI/CD Pipelines

Web/Mobile/API testing

Tools

WireMock

Hoverfly

Mountebank

gRPC

Job description

Job De ion

Job ID: MJ000327

We are looking for a Senior SDET with a solid engineering foundation and an AI application vision. You will work closely with the existing platform team to build and maintain full-stack test frameworks, test data governance, service virtualization, and DevOps pipelines, while exploring how to leverage AI technology to empower traditional testing domains in real-world engineering, jointly enhancing the intelligence of the QEOS system and project delivery efficiency.

Key Responsibilities

• Full-Stack Test Framework Design and R&D: Collaborate with the team to design and develop end-to-end testing frameworks covering Web, Mobile (iOS/Android), and backend interfaces (API/Microservices). Ensure high reusability, low maintenance costs, and good scalability.
• Test Data Governance and Lifecycle Management: Lead the implementation of test data governance solutions. Design and develop automated test data factories to solve key pain points such as dynamic construction, state isolation, sensitive data masking, dirty data cleanup, and multi-environment synchronization.
• Service Virtualization and Mock Infrastructure: Responsible for the design, implementation, and maintenance of enterprise-grade Mock services and Service Virtualization platforms. Support third-party dependency simulation, high-concurrency performance testing stubs, and microservice inter-service abnormal link simulations in complex distributed systems.
• Business Support and Pipeline Engineering: Assist the team in responding to and resolving ad-hoc needs from development and QA teams during daily R&D. Maintain end-to-end CI/CD/CT Pipelines to ensure seamless integration between automated testing and environments.
• Multi-Dimensional Data Dashboard Development: Design and develop data Dashboards covering product quality metrics (defect density, escape rate, etc.), QE health metrics (test coverage, pipeline stability, etc.), and AI cost monitoring metrics (Token consumption, ROI evaluation, etc.).

Requirements

• Framework R&D and Project Delivery Experience: 8+ years of experience as an SDET or Test Development Engineer. Must possess proven project experience in designing and leading the development of Web/Mobile/Backend test frameworks from scratch.
• Test Data Governance Capabilities: Hands-on experience in test data governance for medium to large-scale projects. Proficient in handling production data sampling and masking, snapshot rollbacks, or on-demand data generation based on data factories.
• Expertise in Service Virtualization: Deep understanding of service virtualization architectures. Experienced in configuring and customizing mainstream mocking tools (e.g., WireMock, Hoverfly, Mountebank, or in-house protocol stubs) and handling complex asynchronous protocols (e.g., gRPC, Message Queues, Dubbo).
• AI Quality Engineering Application Capabilities: Ability to collaboratively leverage engineering paradigms—including structured Prompt Chains, advanced RAG retrieval, autonomous Agent self-correction loops, and LLM evaluation/consistency testing (Harness)—to enhance platform and pipeline intelligence.