Backend Engineer - Data Acquisition (Anti-Scam 2B Team)

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."

1. Influential products: What we make are meaningful products that create values for society and defend against frauds.
2. Emphasize self-growth: We encourage technical community activities, subsidize tickets for conferences and workshops so that learning is continuously supported by the company.
3. Unleash your talent: We respect the professional opinions of everyone, encourage team members to discuss with each other, and make awesome products together.
4. 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.

Gogolook is a TrustTech company with the mission of "Build for Trust." We build AI-powered products — including Whoscall and Watchmen — that protect hundreds of millions of people and businesses from fraud across Asia-Pacific. Our work sits at the intersection of real-time threat intelligence, data engineering, and enterprise-grade SaaS, and it has real-world stakes: the systems you build will directly determine how fast we can detect and neutralize emerging scam threats.

As a Backend Engineer (Data Acquisition) on the Anti-Scam 2B team, you will own and evolve Gogolook's web data acquisition infrastructure — the backbone that powers our Watchmen brand-protection platform. You'll tackle one of our most critical cost-and-capability challenges: replacing high-cost crawler dependencies with an in-house, scalable, cost-efficient crawling fleet that supports real-time social media monitoring, fraud signal extraction, and threat intelligence enrichment.

This role requires deep crawler expertise and a high degree of independence. You will be the team's go-to expert for data acquisition, working closely with backend engineers, data engineers, and product to shape how we collect, process, and operationalize web intelligence at scale. As the team's first dedicated crawler specialist, you'll have the ownership to set the technical direction and build the capability for long-term scale.

In the next 6–12 months, your primary mission will be to replace the existing Watchmen crawler dependencies with a purpose-built, in-house crawling system, dramatically reducing operational costs while improving coverage, freshness, and reliability. Beyond the initial migration, you'll expand crawler coverage to new social platforms and fraud signals, build extraction pipelines that feed into Gogolook's fraud database, and develop reusable crawling infrastructure that serves the company's broader B2B product roadmap and AI/ML detection initiatives.