Wealthsimple is Canada’s leading financial innovator. The company offers a full suite of simple, sophisticated financial products across managed investing, do-it-yourself trading, cryptocurrency, tax filing, spending and saving. Wealthsimple currently serves more than 4 million Canadians and holds over $125 billion in assets under administration. The company was founded in 2014 by a team of financial experts and technology entrepreneurs, and is headquartered in Toronto, Canada.
AboutThe Team
We build the products and infrastructure that millions of Canadians trust with their financial lives. Data & Engineering at Wealthsimple spans everything from the client-facing apps to the systems running underneath them — and we hold ourselves to a high bar on both. We move fast, but we build thoughtfully: quality, security, and scalability aren't trade-offs here, they're the standard. If you want to work on hard problems with people who care deeply about craft, you'll fit right in.
AboutThe Role
Financial risk sits at the heart of what makes Wealthsimple trustworthy. The Financial Risk engineering team is responsible for the systems that protect our clients and our platform — from fraud detection to AML compliance to the client experience when things go wrong. It's technically complex, genuinely consequential work, and the team is growing to meet it.
You’ll join a lean, high-ownership team and take on meaningful scope from day one — building fraud defenses, client-facing resolution flows, and the internal tooling that keeps our operations team moving efficiently.
In this role, you’ll have the opportunity to:- Design and build fraud detection systems that protect clients and Wealthsimple from bad actors — across transaction monitoring, account behaviour, and beyond
- Own the client-facing experience for accounts affected by fraud restrictions, including self-serve resolution flows that get clients back on track as quickly and frictionlessly as possible
- Build and improve internal tooling that helps our operations team investigate and resolve fraud cases efficiently
- Develop risk-based product features — such as dynamic transaction limits — that reflect a client's actual risk profile
- Contribute to systems and processes that keep Wealthsimple compliant with anti-money laundering (AML) regulatory requirements
- Work closely with product, design, and operations partners to translate complex risk logic into experiences that feel simple to the people using them
- 4+ years of software engineering experience, ideally in a consumer-facing product environment
- Strong backend engineering skills, with a track record of building reliable, scalable systems
- Experience working on fraud detection, financial risk, or other high-stakes technical domains
- Comfort across the stack — you're as comfortable contributing to a client-facing flow as you are to the systems running underneath it; experience with Ruby on Rails or Kotlin is a strong asset
- Strong software quality practices: you write testable code, take production ownership seriously, and raise the bar in code review
- Clear communicator who can work effectively with non-engineering partners across operations, compliance, and product
- AI-first mindset. You leverage AI tools to accelerate development, improve code quality, and solve problems more effectively. You have already evolved your workflow through AI and are eager to continue innovating.
- Experience with Apache Flink or other stream processing frameworks
- Familiarity with real-time data pipelines and event-driven architecture
- Experience with React Native
- Experience with Temporal for workflow orchestration
- Hands-on experience with AI-assisted development tools such as Claude Code or Cursor, including agentic workflows and MCP integrations
- Background in fintech, banking, or another regulated industry
- You take ownership end-to-end — from writing the code to making sure it works in production
- You're comfortable with ambiguity and operate well in a domain where the rules are real but the problems are rarely clean
- Detail-oriented without losing sight of the bigger picture — you care about correctness, but you also…
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