We are looking for a Data Analyst to support fraud-focused analytics initiatives within a banking environment in Toronto, Ontario. This Long-term Contract opportunity is ideal for someone who can turn complex data into practical insights, strengthen fraud detection approaches, and work across technical and business teams. The role combines hands-on analysis with pipeline and reporting support, while partnering with stakeholders involved in lending, operations, and regulatory processes.

Responsibilities:
• Examine transactional, application, and behavioural data to uncover fraud trends, emerging risks, and actionable patterns.
• Write and optimize SQL-based analysis to support investigations, recurring reporting, and ad hoc business requests.
• Contribute to the design and refinement of fraud rules by translating analytical findings into practical detection logic.
• Support data engineering activities by helping build, validate, and maintain ETL workflows and dependable data pipelines.
• Work with large-scale data environments, including platforms such as Trino and Hive, to prepare and analyze banking data efficiently.
• Partner with business stakeholders to gather requirements, map processes, and align analytical outputs with branch and lending operations needs.
• Interpret lending data in the context of product structures, underwriting practices, and applicable compliance expectations.
• Assist with project delivery activities by coordinating analysis tasks, documenting outcomes, and supporting change-related work within financial services technology initiatives.
• Proven experience in data analysis within a banking, financial services, or fraud prevention setting.
• Strong SQL skills with the ability to extract, transform, and interpret large and complex datasets.
• Hands-on knowledge of ETL processes and experience supporting scalable data pipeline development.
• Familiarity with fraud analytics, including application fraud, behavioural monitoring, or transaction risk analysis.
• Experience working with modern data platforms such as Trino, Hive, or similar distributed query and storage technologies.
• Ability to gather business requirements and translate operational needs into clear analytical deliverables.
• Understanding of lending operations, banking products, underwriting considerations, or related regulatory compliance concepts.

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