I participated in a project from its inception, developing a machine learning model to predict logistical performance indicators, monitor processes, and optimize deliveries in the industry. I worked in both data science and data engineering.Current Work:- Migrating the predictive model to Spark on Azure Databricks.- Automating data for logistical processes.Activities Performed:- Mapped end-to-end processes from data extraction and engineering to model deployment and forecast availability.- I supported the business team in understanding technical concepts, detailing the product (predictive model), and applying its predictive results to operational and business areas.- Refactored project code to enhance modularity and scalability.- Conducted comprehensive Exploratory Data Analysis (EDA).Responsible for automating and integrating systems to ensure continuous data updates across multiple corporate platforms. I develop and maintain ETL workflows and APIs for automation.I developed a Python utility module to streamline the creation of ETL pipelines and integrations with Microsoft web applications. The module provides secure authentication and connections, supports data reading, manipulation, and loading using Pandas, and is designed for reusability and flexibility. It ensures consistent and reliable integration processes with validation functions to maintain data integrity.Technologies: REST, Azure Functions, Git, DevOps, Office 365 Python Client Library