I work in data for about 4 years across public and private sectors, focused on intelligence and data engineering. My Economics background gave me a strong quantitative foundation that I apply in practical, outcome-driven analytics.
In my current role, I lead analytics within the Technical Coordination of Data Intelligence (CTID) at the Municipal Transportation Department (SMTR) of the City of Rio de Janeiro. I analyze public transportation and urban mobility data with Python and SQL on BigQuery, and I build and maintain data pipelines using dbt models.
I also take ownership of reporting, documentation, and engineering practices—constructing analytical reports with Quarto and Markdown, versioning with Git/GitHub, and managing work through Kanban. I’ve delivered concrete results, including improving the success rate of trips from 70% to 96% and automating monitoring/classification across more than 7.8 million trips.
Earlier, I strengthened my BI and analytics delivery by developing and supporting dashboards (Looker Studio, Power BI, and Apache Superset) and integrating datamarts with PostgreSQL and Amazon Redshift. Across projects, I’ve focused on reducing bottlenecks—cutting data processing time from 4 hours to 15 minutes—and on building reliable, well-documented data flows.
