Large data sets were transformed into actionable insights, empowering marketing and operations teams to make data-driven decisions. SQL and Tableau were used to craft clear, impactful reports, while SSIS was employed to update predictive models monthly. Automation of Python-based ETL processes enhanced data integration, reducing loading times by 25%, and providing deeper insights into customer behavior and marketing effectiveness.

Data solutions were developed for a major US healthcare client by analyzing requirements, designing optimized data structures, and creating mapping sheets, resulting in a 40% reduction in processing time and improved data accuracy. Managed end-to-end ETL workflows in Azure Data Factory across six regions, integrating 1-2 GB of daily and monthly files into Azure Data Lake Gen2 for seamless consolidation. Transformed over 500,000 lines of SAS code into optimized SQL, boosting query performance by 25% with advanced analytical functions and common table expressions. Leveraged Databricks to process large datasets with complex SQL queries and Python scripts, achieving a 79% improvement in processing efficiency.

Automated data extraction and transformation with Python, reducing manual work by 70%. Developed scripts for over 200 test cases using the PYATS framework, cutting execution time by 70%. Built scalable data models for network performance monitoring, improving incident resolution efficiency by 30%. Collaborated across teams to create data-driven solutions for testing wireless network features, contributing to product enhancements. Conducted root cause analysis for network issues, resolving over 50 critical bugs and significantly boosting system reliability.