I started by designing a relational database schema for a banking system, carefully modeling entities like users, accounts, transactions, and user roles. This laid the foundation for generating realistic synthetic financial data. Using Python, I created a data generation script to populate the database with synthetic banking records - simulating customer profiles, account balances, transaction histories and more. This synthetic data was then loaded into a PostgreSQL database. To enable cloud-based analytics, I set up a pipeline to sync the PostgreSQL data to Google BigQuery. This allowed me to leverage BigQuery's powerful querying capabilities for large-scale data analysis. Finally, I connected BigQuery to Power BI to create an interactive dashboard visualizing key banking metrics. The resulting report includes account type distributions, historical balance trends, and geographic analysis of customer data. This end-to-end project showcased the power of combining robust data modeling, synthetic data generation, cloud data warehousing, and business intelligence tools to derive actionable insights from complex financial datasets.