About us
Gogolook is a leading TrustTech company founded in 2012 and listed on the Taiwan Stock Exchange in 2025 under the stock code 6902. With "Build for Trust" as its core value, the company has expanded its business from Asia to Europe and America. Gogolook’s AI technology is built on the world's largest database of digital scam data, encompassing phone numbers, websites, virtual currency wallet addresses, and other factors.
The company provides diverse anti-scam and fintech services for both consumers and businesses. Its anti-scam offerings include the digital anti-scam app "Whoscall" and a range of enterprise scam prevention solutions in combination with "ScamAdviser."
The Fintech BU empowers consumers through data-driven financial services and inclusive lending: Roo.Cash（袋鼠金融）offers transparent matchmaking for financial products like credit cards, while JUJI（招財麻吉）provides an innovative microloan service for rapid, convenient funding during urgent financial needs.
A foundation member of the Global Anti-Scam Alliance (GASA), Gogolook has also teamed up with a number of institutes such as the Taiwan National Police Agency Criminal Investigation Bureau, the Financial Supervisory Service of South Korea, Thai Royal Police, the Fukuoka city and Shibuya City government, the Philippines Cybercrime Investigation and Coordinating Center, and the Royal Malaysia Police and state government to fight scam, dedicated to creating a "scam-free environment."
Why you should join Gogolook
- Influential products: What we make are meaningful products that create values for society and defend against frauds.
- Emphasize self-growth: We encourage technical community activities, subsidize tickets for conferences and workshops so that learning is continuously supported by the company.
- Unleash your talent: We respect the professional opinions of everyone, encourage team members to discuss with each other, and make awesome products together.
- Transparent culture: We publicly share the company's information to all, every member can read and feedback, and become a part of participating in the proposal.
We are looking for a Backend SDET for the Anti-Scam BU Common Service Team. Your primary focus will be developing automated tests and internal testing tools using Python to continuously measure the quality of backend services and data pipelines, shifting away from manual regression.
You will work directly with PMs, RDs and SREs, taking ownership of API and data pipeline test design and implementation, test framework maintenance and extension, and ensuring the stability of automated tests in CI/CD.
Our test frameworks, tools, and CI scripts are exclusively written in Python. We expect your Python proficiency to be at a level where you can write maintainable test code that others are willing to take over.
We expect you to make independent decisions within your scope of responsibility and proactively communicate any identified risks. Cross-team architectural decisions and priority trade-offs will be discussed directly with the QA Lead.
Key Responsibilities
API Testing: Design and implement automated tests for API behaviors, covering functional, regression, and contract levels. Since multiple product lines depend on the Common Service, contract verification for APIs and event schemas is a key focus of this role.
Data Flow & Pipeline Validation: Design and implement automated validation for queues, events, scheduled jobs, and data pipelines. This covers data correctness, completeness, duplication/loss prevention, processing latency, and idempotency (result consistency upon rerun).
Test Environment & CI Pipeline: Maintain and extend automated tests within the existing pipeline architecture, and propose improvements for execution time, stability, and coverage. Maintain the reliability of existing tests, including identifying and resolving flaky tests. Deploy test environments following standard procedures and troubleshoot environment anomalies independently.
Test Framework & Internal Tools: Maintain and expand the test framework based on existing project structures and conventions, including shared fixtures, type hinting, and test data management. Additionally, develop internal tools to automate repetitive manual verification tasks for RDs or PMs.
Performance Testing: Participate in the implementation of performance and load testing (Locust, JMeter), including script writing, scenario configuration, and result interpretation.
AI Integration in Workflows: Leverage coding agents and LLMs for test writing, test data generation, log analysis, and failure attribution. Document reusable prompts, specifications, and guidelines to ensure stable output quality from these tools across the team.
Minimum Qualifications
3+ years of experience in software development or test automation.
Proficient in Python development, including the pytest ecosystem, virtual environments, package management, and type hinting; able to write well-structured, maintainable code.
Experience working with asynchronous processing systems (queues, events, background scheduling) or data pipelines, with a solid understanding of the testing challenges introduced by eventual consistency.
Solid understanding of HTTP/API, databases, and the operational principles of AWS cloud services (e.g., DynamoDB, Lambda, Systems Manager).
Hands-on experience with CI/CD practices, such as GitHub Actions.
Strong grasp of QA fundamentals: understanding the appropriate use cases for different testing levels, coverage strategies, and knowing what is not worth testing.
Proven integration of AI tools into daily workflows, with the ability to clearly articulate the scenarios where they are effective or ineffective.
Proactively communicates technical risks; conscious of technical debt, willing to track it, and actively participates in resolving it.
Able to find answers independently when information is incomplete and adapt approaches based on past mistakes.
Preferred Qualifications
Experience with contract testing, test environment provisioning, and test data management.
Experience using observability tools (Datadog, Grafana).
Basic operational knowledge of Docker, Kubernetes, or Terraform.
Proven track record of developing internal tools that were actively adopted by other teams.
Experience in quality validation of AI features (e.g., evaluation methods for non-deterministic outputs).
Ability to conduct technical discussions in both English and Mandarin.
