Production SQL using cascading CTEs and Window Functions to detect fraud signals — the same query patterns used in real financial operations environments.
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Financial analytics dashboard showing real-time KPIs: transaction volume, fraud rate, and revenue metrics. Built on top of complex SQL pipelines running against 50K+ financial records.
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43
High-performance financial data ecosystem with automated ETL pipelines, credit score modeling, and AML anomaly detection. Built end-to-end in Python and PostgreSQL — ingests raw financial data, cleans it, and flags suspicious transactions using patterns from my Goldman Sachs experience. Stack: Python · PostgreSQL · dbt.
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34
End-to-end financial analytics dashboard tracking transaction volume, fraud rate, and account activity in real time. Built to give operations teams instant visibility into key risk metrics — the same type of monitoring I worked with at Goldman Sachs.