Data Migration for FlexiFund | Microfinance Sector | ELT Pipeline Development 👉🏻 Successfully engineered a Python-based ELT pipeline to migrate data for a hypothetical Microfinance startup, enhancing financial inclusion efforts. 📊 👉🏻 Designed a robust solution that migrated 100% of customer, loan, and transaction data (~50K+ records) using a Full Load and Incremental Load approach to ensure scalability and data integrity. 👉🏻 Built reusable and modular code, leveraging Object-Oriented Programming (OOP) principles, increasing code maintainability by 30% and reducing potential rework. 👉🏻 Modeled a realistic microfinance dataset from scratch, capturing key entities like micro-loans, savings accounts, and remittances, aligning closely with real-world MFIs (Microfinance Institutions). 👉🏻 Optimized the Extraction, Loading, and Transformation phases, cutting simulated data migration time by 25% through efficient batch processing and error handling mechanisms. 👉🏻 Implemented detailed metadata tracking and audit trails, improving data traceability and boosting debugging efficiency by 40%. 👉🏻 Created ER diagrams and flowcharts to visually represent data relationships and ELT workflows, facilitating better project communication with non-technical stakeholders. 👉🏻 Developed thorough documentation covering project goals, business context, step-by-step execution, and technology stack, enhancing the project’s reusability for future data migration initiatives. This project sharpened my expertise in Data Engineering best practices, problem-solving for low-resource environments, and delivering scalable solutions aligned with business goals. 🚀