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 Analytics Engineer to lead the analytical efforts of our Real-time Fraud Monitoring Team.
Working within one of Mollie’s newest verticals, our team is dedicated to protecting our account holders from financial crimes. We’re on the front lines, developing scalable, automated risk decision-making tools that operate in milliseconds. Here, real-time data meets real-world security.
This role isn’t just about technical expertise; it’s about addressing critical business challenges. You’ll use your technical skills to design, build, and optimise intricate data pipelines and analytical solutions for fraud detection. You’ll work with Mollie’s complex, real-time data flows, transforming raw data into actionable insights that directly combat financial crime and enable swift risk decisions.
You’ll play a key role in driving impactful solutions from concept to deployment, partnering closely with our engineering, product, and operations teams to ensure our analytical capabilities are seamlessly integrated. Your efforts will directly contribute to protecting our account holders, turning data into the shield that safeguards our business.

• Data product development: Build and maintain robust, performant, easy-to-use data products and dashboards to monitor key fraud metrics and help us make decisions
• 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.
• Experimentation & prototyping: Develop and test new analytical approaches and models through experimentation and prototyping to improve fraud detection and risk assessment.