• Developed a Streamlit-based Python web application to handle data redundancies in extensive SQL databases, successfully simplifying data management.

• Architected a backend framework in Python to mitigate data redundancies in SQL databases and migrated data to Snowflake DB, aiming for optimal cloud integration and scalability, and achieved a more resilient and adaptable architecture.

• Optimized deduplication algorithms and queries to ensure data consistency and accuracy, leading to a seamless integration and maximizing system performance. Reduced average query execution time by 30%.

• Refactored and modularized critical sections of the Python codebase by abstracting redundant code patterns into versatile utility functions to improve application's maintainability and scalability. Reduced the codebase footprint by 20%.

• Engineered a web-based query interface to abstract SQL complexities, facilitating non-technical stakeholders in executing database operations, thus democratizing data access.

• Implemented Pytest-based unit tests to validate the robustness and effectiveness of various application algorithms and functionalities, increasing testing coverage by 30%.

• Developed ETL workflows using Alteryx to optimize real-time data processing, achieving an 80% reduction in execution time and transforming a multi-resource task into a streamlined, autonomous operation.