Working knowledge of SQL, Snowflake, relational databases, data extraction, data validation, and data quality concepts; Working knowledge of Microsoft Excel, including pivot tables, formulas, data cleaning, and analytical functions; Knowledge of data visualization, dashboarding, or reporting tools such as Power BI, Tableau, or similar platforms preferred; Familiarity with Python, R, SAS, model monitoring, statistical analysis, or machine learning concepts used in fraud detection preferred but not required; In-depth knowledge of fraud typologies and associated bank fraud schemes, including online account opening, account takeover, social engineering, mule activity, mobile, digital, check, ACH, wire, debit card, and payment fraud; Familiarity with payment types and digital banking products, including Zelle, Bill Payments, Bank-to-Bank payments, ACH, wires, checks, debit cards, and related customer authentication controls; In-depth knowledge of BSA/AML regulations related to suspicious activity monitoring, escalation, and reporting; Strong understanding of bank operations, including navigating banking systems, interpreting teller and customer transactions, and evaluating account relationships; CAMS (Certified Anti-Money Laundering Specialist), CAFP (Certified AML and Fraud Professional), CFE (Certified Fraud Examiner), or data analytics-related certification preferred. This role analyzes large and complex datasets, fraud alerts, customer and transaction activity, and emerging fraud trends to support timely risk-based decisions, reduce fraud losses, strengthen controls, and ensure regulatory expectations are met.