Orchestrated the extraction, transformation, and loading (ETL) of over 20 million delivery records using Apache Spark and AWS Glue, ensuring a streamlined data pipeline for analysis.• Employed regression and classification algorithms, enhancing route planning accuracy and reducing average delivery times by 20%.• Integrated machine learning models with delivery systems using AWS cloud services, ensuring scalable and efficient real-time operations, with deployment pipelines managed via GitHub Actions for CI/CD.• Designed and maintained dynamic dashboards in Power BI, providing real-time operational insights to management and stakeholders.• Authored detailed monthly reports on analytics outcomes, highlighting a 15% increase in customer satisfaction and a 10% reduction in operational costs.• Demonstrated strong Excel proficiency, including the use of advanced shortcuts, formula functions, and data manipulation techniques.• Conducted performance tuning of SQL queries, reducing execution times and enhancing database performance.

Managed and preprocessed over 10 million records of historical ride data, ensuring a clean and robust dataset for analysis,and collaborated with teams by tracking data quality metrics and updates in Google Sheets.• Conducted Exploratory Data Analysis (EDA) on large-scale datasets, uncovering trends that led to a 15% improvement in demand forecasting accuracy.• Developed and refined machine learning models, leading to a 20% reduction in customer wait times and a 25• Engineered over 30 new data features, enhancing model predictiveness and contributing to a 10% increase in overallsystem efficiency.• Implemented a real-time model monitoring system, reducing system errors by 40% and ensuring continuous model performance optimization.• Created over 50 custom data visualizations in Power BI, effectively communicating complex analytical findings to non-technical stakeholders, while linking underlying datasets and results to Google Sheets for transparency and cross-functional accessibility.