Developed and managed the Data Warehouse for the Industry Observatory, integrating and curating diverse data sources for three companies. Designed and implemented automated ETL data pipelines to streamline business processes and enhance the shared data warehouse.Created web scraping pipelines utilizing AI analysis to extract data for business processes, significantly improving coverage and speed.

Designed, developed, and maintained ETL processes using Shell Script, Informatica PowerCenter, and Oracle, ensuring high data quality and integrity.Worked in the migration of telecommunications data, managing large datasets and executing complex merging checks to ensure seamless data integration.Collaborated in critical migration projects, identifying and resolving issues to minimize downtime and maintain project timelines.

Developed and managed high-frequency ETL pipelines capable of handling large volumes of data from web crawlers, APIs, and unstructured sources using Python, Airflow, SQL Server, and Azure.Successfully created two products for the agricultural chemical industry in Brazil, which were rapidly adopted by major industry players.Enhanced internal data processing pipelines through automation, significantly accelerating critical delivery timelines and improving overall operational efficiency.

Using the R programming language and the simmer package, a simulator was built to model the production process of blood components in a blood bank. The Shiny framework was employed to create an interactive visualization of the simulation results.