Engineered a scalable nationwide model using SQL, Python, and Power BI, delivering over R$30M added value by optimizing vehicle KPI monitoring and management of all vehicles across the group's companies. Designed and developed a SQL and Power BI logistics dashboard integrating inventory control, automated purchasing alerts, cost and expense monitoring, and stock variation analysis, reducing replenishment lead time by 30%, improving operational visibility, and enabling faster data-driven supply chain decisions through centralized KPI management.

A 15% reduction in the return and cancellation rate and a 20% reduction in the monthly closing time for the vehicle sales team was achieved through the creation and implementation of a national analytical dashboard using BI tools to monitor KPIs for vehicle purchase returns and cancellations during strategic periods of the month. Reduced vehicle sales delinquency rates by 18% by developing and implementing a SQL and Python analytics dashboard focused on customer payment tracking, overdue accounts, and accounts receivable monitoring.

Conducted financial data analysis across multiple group companies, identifying process inefficiencies and cost reduction opportunities that improved EBITDA by 12%. Automated recurring financial reports using Python and SQL, reducing monthly closing time from 5 days to 2 days while increasing data accuracy and traceability.

Built pipelines using SQL and Python (Pandas), reducing weekly data processing time by 76% (3h -> 40min) and eliminating manual errors. Conducted price elasticity analysis on 30+ SKUs using statistical methods, driving pricing changes that increased contribution margin by +4.5% without volume loss.