I'm a data analyst with a software engineering background, transitioning from full-stack web development into analytics. I build end-to-end data projects using SQL, Python, and Power BI — combining analytical rigor with the engineering discipline I've developed over years of shipping production code.

I take raw data and turn it into insights. I'm comfortable moving across the full stack:

• SQL — writing business-driven queries, window functions, CTEs
• Python — pandas EDA, statistical analysis, visualization (matplotlib, seaborn)
• Power BI — interactive dashboards, data modeling, stakeholder-facing reporting

My engineering background means I care about reproducibility, clean code, proper data pipelines, and knowing why a number matters, not just what it is.

Building a portfolio of real analytical projects, each one integrating SQL → Python → Power BI. My latest project analyzed a Brazilian e-commerce dataset of 99,000+ orders across 9 relational tables — uncovering customer segmentation patterns, logistics anomalies, and category-level revenue performance.

📊 Olist E-Commerce Analysis — SQL + Python + Power BI analysis of a 99,000+ order dataset (R$15.4M GMV). Includes RFM customer segmentation, a data-quality anomaly investigation, and an interactive 5-slicer dashboard.

📺 OTT Streaming Analytics — SQL + Python + Power BI analysis of content trends and platform performance across 2,500 titles. Includes interactive dashboard, detailed findings, and full reproducible pipeline.

(Web dev projects below — solid foundation, but my focus is now on data.)

Data: SQL · Python (pandas, matplotlib, seaborn) · Power BI · Excel · SQLite
Engineering: JavaScript · TypeScript · React · Next.js · Node.js · Git

If you're interested in data work, analytics projects, or the web-to-data transition story, reach out — I'm always up for discussing methodologies, tools, or interesting datasets.

Last updated: 3 September/2026