Matheus Amorim

I’m a data professional with a Computer Science background and experience across the full data lifecycle—from pipeline development to insight extraction. I build ETL/ELT flows, process large volumes of data with PySpark and Databricks, and focus on performance, scalability, and governance.

In my Data Analyst role, I map requirements with business areas and translate them into pipelines, metrics, and analyses. I use Python and advanced SQL (CTEs, window functions, aggregations) to clean and transform scalable analytical bases, then apply exploratory and statistical methods to identify trends and assess impact on business indicators.

I also deliver decision-ready outputs through executive dashboards in Power BI for KPI tracking and information democratization. Alongside analytics, I work with data quality validation routines and build product-focused measurement (A/B testing, segmentation, conversion and consumption metrics), and I’ve completed an MLOps capstone using MLflow and Prefect to emphasize reproducibility and traceability.