Lucas Bruno

Architected resilient Medallion lakehouses in Microsoft Fabric using PySpark and Spark SQL, creating business-critical metrics not stored in source systems. Built OAuth2/SFTP ingestion, scaled semantic models with row-level security in Power BI, and led debugging of a night pipeline with data issues impacting execution and financial values.

Identified that a frequently reported data quality problem was largely a false alarm, cutting its real impact by more than 90%. Traced root causes behind inflated volume metrics, reduced anomalies by more than 99%, validated a granularity fix, and replicated the data quality analysis suite to two additional countries.

Refactored critical agribusiness ETL jobs, reducing processing time from hours to minutes. Eliminated manual data collection and delivered credit decision reporting in Power BI and Tableau.

Built predictive dashboards in Power BI to monitor global machinery, reducing the analysis cycle by 40% for international stakeholders. Supported decision-making for accounts including Volvo and John Deere.

Automated ETL and dashboards for investment projects above R$1 billion, accelerating the reporting cycle by 70%. Improved delivery speed for investment project reporting.

Implemented RPA and Python solutions, increasing team productivity by 30%, and built financial dashboards in Power BI. Supported business growth by enabling financial reporting that contributed to a 25% revenue increase.

Bacharel em Ciência de Dados e Machine Learning pela UniCEUB.

Certificação Databricks Lakehouse Fundamentals pela Databricks Academy.

Certificação IBM Cognos Analytics Authoring pela IBM Training.

Big Data & Data Engineering Track pela Data Science Academy.