Data Engineer

We are seeking a Senior Data Engineer - Fraud Analytics to design, build, and maintain data solutions that support the research, detection, and analysis of fraud events. This role partners closely with fraud analysts, investigators, and business stakeholders to transform large, complex datasets into actionable insights that reduce fraud risk and improve detection strategies. Key Responsibilities Desi gn, develop, and optimize data pipelines to support fraud research and analytics use cases. Research and analyze fraud events by querying and correlating structured and unstructured data across multiple platforms. Write complex, high-performance SQL queries to extract, transform, and analyze large datasets. Work extensively with Oracle databases to support enterprise-scale fraud analytics. Utilize MongoDB for handling semi-structured and unstructured fraud-related data. Investigate data anomalies, identify fraud patterns, and support root-cause analysis. Partner with fraud operations, compliance, and analytics teams to translate business questions into technical data solutions. Ensure data accuracy, consistency, and reliability across fraud datasets. Document data models, logic, and findings clearly for both technical and non-technical audiences. Communicate findings effectively through reports, presentations, and stakeholder discussions. Support continuous improvement of fraud detection and monitoring processes through data-driven insigh ts. Required Qualifications 5+ y ears of experience in data engineering, analytics, or related technical roles. Advanced proficiency in SQL (complex joins, subqueries, performance tuning, data validation). Strong hands-on experience with Oracle databases. Working knowledge of MongoDB or similar NoSQL technologies. Experience researching fraud events, financial anomalies, or suspicious activity preferred. Strong analytical and problem-solving skills with attention to detail. Excellent written and verbal communication skills. Ability to work independently while collaborating across cross-f unctional teams. Preferred Qualifications Exp erience supporting fraud, risk, compliance, or financial crime analytics. Exposure to scripting or data-processing languages such as Python. Experience working in regulated or financial services environments. Strong investigative mindset with ability to manage multiple res earch efforts simultaneously. Certifications Rel evant certifications in data engineering, databases, or analytic s preferred.