Delivered an end-to-end banking analytics solution on a dataset of 3000+ customer records, leveraging SQL, Python, and Power BI to transform raw data into actionable business insights. Engineered clean and reliable data by resolving encoding issues and designing derived metrics such as Total Balance and Engagement Flag, enabling accurate performance analysis. Developed advanced SQL queries to compute KPIs, perform customer segmentation, and analyze engagement using business-driven logic. Implemented window functions (RANK) to identify high-value customers. Performed exploratory data analysis (EDA) using Python (Pandas, Matplotlib, Seaborn) to uncover behavioral patterns and correlations across customer segments. Designed a multi-page interactive Power BI dashboard with KPI cards, filters, and navigation, enabling stakeholders to monitor deposits, engagement, and customer distribution in real time. Key Business Insights: • High-value customers contribute a significant share of total balance • Customers with multiple credit cards show higher engagement levels • Age group 25–40 represents the most active and valuable segment • Strong positive relationship between deposits and credit card usage Impact: Enabled data-driven decision-making by providing clear visibility into customer value, engagement trends, and financial behavior. Tools & Technologies: SQL | Python | Power BI | Excel