At Mollie, we’re on a mission to make payments and money management effortless for every business in Europe. We started 20 years ago when we launched a more direct, affordable way for companies to get paid. That provided an alternative to the frustrating, overpriced solutions that banks offered at the time.
Today, we serve more than 250,000 businesses across Europe with an all-in-one solution that simplifies payments and money management. And we’re a 850-strong team of product, finance, support, commerce, and engineering specialists working across Europe – from Lisbon to London.

YOUR OPPORTUNITY

We’re looking for an experienced and motivated Senior Analytics Engineer to support the analytical efforts of our Financial Crime Monitoring Team. The team is working on a diverse and impactful problem space, with a key focus on detecting, monitoring, and preventing financial crime.
You will leverage your strong technical skills in analytics and hands-on knowledge of Mollie’s complex data to solve pressing business challenges. You’ll be playing a key role in shaping Mollie’s approach to financial crime, and driving scalable solutions in collaboration with engineering, product, and operations.

• Data product development: Build and maintain clean, robust, performant, and user-friendly data products, decision-making tools, dashboards to monitor key metrics, fraud signals, transaction monitoring rules and alerts.
• Data analysis & interpretation: Analyse merchant behaviour and transaction data to detect patterns, anomalies, and potential fraud indicators to inform Mollie’s risk strategy.
• Data visualisation & storytelling: Create compelling data visualisations and narratives to effectively communicate your findings to technical and non-technical audiences using various mediums, e.g. presentations, reports, memos, etc.
• Documentation & knowledge sharing: Create and maintain comprehensive documentation for data products, processes, and analyses to facilitate knowledge sharing and onboarding of new team members.
• Data quality assurance: Implement data quality checks and processes to ensure the accuracy, completeness, and reliability of data used for analysis and decision-making.