• Lead the migration of fraud reporting and workflows into a fully automated, scalable data pipeline. • Design, build, and maintain analytics-ready data models using dbt that support fraud monitoring, investigation, and strategy. • Partner with Data Engineering to ensure reliable ingestion, data quality, and documentation of fraud-related datasets. • Develop and maintain dashboards and reporting that track key fraud metrics such as fraud rates and loss, chargebacks and disputes, bonus abuse, promo abuse, and multi-accounting, and KYC and account integrity signals. • Surface emerging fraud patterns and anomalies through proactive analysis. • Write performant, production-quality SQL across large, complex datasets. • Build modular, reusable dbt models that enable self-service analytics for Fraud & Risk teams. • Implement testing, freshness checks, and documentation to ensure trust in fraud data. • Work closely with Fraud & Risk, Payments, Customer Support, Product, and Engineering to align on definitions, metrics, and investigative workflows. • Provide actionable insights to fraud leadership to inform risk rules and thresholds, tooling and vendor evaluations, process improvements and operational tradeoffs.