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Data Analyst, AML & Regulatory Compliance

Nubank · Ciudad de México

Data and MLOperationsOperationsPosted Last week
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About this role

ABOUT NU Nu is the leading digital bank in Latin America, serving 135 million customers across Brazil, Mexico, and Colombia. The company has been leading an industry transformation by leveraging data and proprietary technology to develop innovative products and services. Guided by its mission to fight complexity and empower people, Nu caters to customers’ complete financial journey, promoting financial access and advancement with responsible lending and transparency. The company is powered by an efficient and scalable business model that combines low cost to serve with growing returns. Nu’s impact has been recognized in multiple awards, including Time 100 Most Influential Companies, Fast Company’s Most Innovative Companies, and Forbes World’s Best Banks. Visit our Institutional Page https://www.nu.com/2026-en About the Role As part of Nu's Regulatory Solutions team, you'll help protect the trust millions of customers place in us every day. Your work on KYC, Enhanced Due Diligence, and Judicial Orders analytics keeps our growth sustainable and our compliance airtight across Brazil, Mexico, and beyond. You'll turn complex regulatory data into dashboards, hypotheses, and insights that guide how Nu meets its legal and customer obligations. Every trend you catch and every root cause you uncover helps us stay diligent, flexible, and resilient - the way Nu was built to be. This is a role for someone who finds meaning in the details: turning ambiguous regulatory questions into clear, data-backed answers that shape how we serve our customers responsibly. You'll be responsible for - Build dashboards and visualizations that surface trends and deviations in KYC, EDD, and Judicial Orders indicators over time - Identify and correlate relevant data sources (QuickSight, BigQuery, SQL) to formulate and test hypotheses about regulatory operations - Write optimized SQL queries to solve complex data problems across large transactional datasets - Apply Python, Scala, or similar language

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