Golang Backend Engineer, Financial Product Fraud

Description

Our Global Structure

Deliveroo is now part of DoorDash, bringing together teams with even greater reach, scale, and ambition. Depending on your role, you may collaborate with teammates, systems, and leaders across DoorDash and Wolt. Together, we’re unlocking new possibilities as one global team.

Your job is to make sure every payout and transaction lands with the right person.

We have spent years getting very good at protecting the things a marketplace has always had to protect: checkout fraud, account takeover, courier abuse. But we are no longer only a marketplace. We now issue banking products, lend to merchants, move instant payouts to couriers, and collect cash on the doorstep. Every one of those is a new way for money to leave the platform.

This is a new team, and it owns that surface: fraud wherever money moves through the platform. Who receives it, who lends it, who collects it, and who holds it in a financial account. Think instant payout rails, merchant cash advances, and platform-issued banking and earned-wage products serving millions of couriers and merchants. These are live loss surfaces, and the volume grows with every financial product we launch.

We are now looking for a Backend Engineer (Golang) to build that protection. You will design and ship real-time detection and automated enforcement for our financial products across DoorDash, Wolt, and Deliveroo. The work is high-throughput, low-latency, and unusually consequential: a rule you ship on Monday can stop a serious loss by Friday. It is also close to greenfield, so you will be setting the patterns every future financial product inherits on day one.

This role will be based in London , UK as one of the founding engineering roles in our London hub, working closely with our teams in Helsinki and Berlin.

Responsibilities

• Build and maintain high-throughput, low-latency backend services in Go that score financial events in real time, including payout enrollments, bank account links, credit applications, and account-to-account transfers.
• Design fraud detection and automated enforcement for our financial products end to end: from signal collection through to the decision that blocks a fraudulent payout.
• Build on and extend our shared fraud decisioning platform, so signals, rules, and models work across all three of our brands instead of being rebuilt three times.
• Partner closely with risk analysts, data scientists, and product managers to turn observed fraud patterns into shipped detection, and to know when a rule, a model, or a product change is the right answer.
• Work across team boundaries with our account security, payments fraud, and financial product teams. We build on their signals rather than rebuild them, so clean interfaces matter.
• Contribute to architectural discussions and technical decisions as we establish the fraud infrastructure baseline for financial products, with support from senior engineers on the team.
• Write well-tested, observable code and help the team hold a high quality bar. In fraud, a silent failure is a loss event.
• Our expectations:
• 3 to 6 years of professional software engineering experience, including backend services you have shipped and owned in production.
• 1+ years of production experience in Golang, or strong experience in another backend language and real enthusiasm for picking Go up quickly.
• A working understanding of low-latency, high-throughput distributed systems. Real-time decisioning comes with a latency budget, and you are keen to learn to build inside one.
• Hands-on experience with SQL and/or NoSQL databases, and some exposure to event-driven architectures (Kafka or similar).
• Willingness to operate what you build, including a share of on-call, with a pragmatic approach to reliability.
• Uses AI tooling in day-to-day development and can critically evaluate its output for correctness and quality.

Requirements

• Experience in fraud, risk, payments, lending, or financial crime, especially money movement fraud, bank account linking fraud, payout abuse, or lending fraud.
• Experience with rules engines, real-time feature stores, or ML model serving in a decisioning path.
• Background in Kotlin, Java, Python, or Node.js alongside Go.
• Familiarity with cloud infrastructure (AWS, GCP).
• Experience working across multiple brands, regions, or regulatory regimes.
• What we value
• A product-oriented mindset. You think beyond the ticket, understand the "why" behind the work, and take a high level of responsibility for outcomes rather than output.
• Comfort with ambiguity. This is a new team on a new surface; parts of the charter are still being drawn, and you will help draw them.
• A track record of owning a project or feature end to end, especially in fast-paced environments.
• Clear and proactive communication, with a collaborative spirit. We work across three companies and several time zones, so we move fast, but we succeed together.
• An adversarial instinct. The best fraud engineers enjoy thinking like the attacker.