Lead Data Analyst- Card Fraud

About this role

We’re Capital on Tap 👋
💳 Capital on Tap started because small businesses were underserved. Big banks were slow, their products weren't fit for purpose, and small business owners often couldn't access what they needed. We set out to fix that.

Today we're a financial platform - not just a credit card company. We offer a best-in-class business credit card, SME-focused spend management platform, a savings product that hit £1 billion in funds within its first year, and a growing suite of tools and financial products that make running a small business easier.

1,000+ employees, £20bn in annual card spend, 200,000+ customers, 17,000+ Trustpilot reviews averaging 4.7 stars, and we're profitable. We’ve done a pretty good job so far, but we’re just getting started!

🏢This is a Hybrid role in our London (EC1Y 8LZ) Offices 3 days per week.

What You’ll Be Doing

We're looking for a Senior/Lead Card Fraud Data Analyst/ Analytics Manager to join our Fraud team. This is a hands-on, high-ownership role - you'll take direct responsibility for our fraud systems end-to-end, from onboarding fraud through to card fraud, and play a central part in building the capabilities we need as we launch new products.

• Write complex SQL queries to interrogate transaction and customer data, identify fraud signals, and surface emerging attack patterns.

• Design and build fraud detection rules from scratch, set thresholds based on data, and iterate based on live performance.

• Own rules end-to-end. You spot the pattern, you build the detection, you monitor the outcome.

• Work across card and lending data to understand how our customers behave and where fraud risk sits in our specific product set.

• Contribute to fraud strategy as the team grows, including input into ML-based detection further down the line.

We’re Looking For 🔎

• A strong data analyst background in a fintech or digital lending environment. You know what SMB or consumer transaction data looks like and you're comfortable. working with messy, high-volume datasets.

• SQL you can actually use. CTEs, window functions, the difference between WHERE and HAVING. You'll be tested on this at case study stage.

• A genuine builder instinct. You don't wait to be told there's a problem. You find it in the data and you fix it.

• Experience working end-to-end on analytical problems. Not handing logic to a…