The kind of data engineering content in Portuguese where you can tell the person actually lived what they’re writing about, that’s hard to find.
Search right now. You’ll find a lot of solid material to start with: translated articles from English blogs, tutorials grounded in the official docs, courses teaching Pandas on simple datasets. All of that has its place, it’s where most people start, and the people producing it are doing important work.
What’s still hard to find is someone telling you how they decided to use Delta Lake instead of Parquet in an environment processing hundreds of millions of daily transactions. Or when Medallion Architecture helps and when it just gets in the way. Or how LGPD (Brazil’s data privacy law) actually changes the way you design an ingestion layer.
That’s the gap I want to help fill.
Who I am, by what I’ve built
I won’t list certificates. I’ll tell you what I’ve shipped.
I’m a senior data engineer with 8+ years of experience. I started in data quality at a major Brazilian bank, then moved to a global-scale Brazilian fintech building ETL pipelines, worked on a big-tech project in Silicon Valley through an international tech consultancy, and today I’m back in the Brazilian banking sector. (Full résumé on the /sobre/ page.)
My core stack is Databricks. Not because I read the docs. Because it’s what runs in production where I’ve worked.
In 2026 I started a master’s in applied computational methods. My research is on AI-driven predictive monitoring for critical operational systems. Everything I learn there I plan to bring here, translated into something useful for engineers working with real data.
Why crypto entered the story
A few years ago I started studying on-chain analytics. And I noticed something that few people seem to be saying clearly: crypto, in large part, is a data engineering problem that’s still poorly solved.
The data is all there. On-chain, open, public. But most people investing in crypto don’t know how to process it, and many data engineers still aren’t looking at it.
So I decided to build a crypto AI agent from scratch. In public, documenting every architecture decision. Using the same tools I use at work: real pipelines, rigorous backtesting, actual statistical models. No hype, no get-rich-quick promises.
What you’ll find here
Three tracks, one newsletter.
The first is production data engineering: Databricks, Delta Lake, Spark, dbt, Airflow. Real architecture decisions, mistakes I made and what I learned, Brazilian context where it’s relevant (LGPD in practice, cloud cost reality, what data actually looks like inside financial institutions).
The second is the crypto AI agent, built in public. Architecture, code, backtesting, on-chain analysis. Every step documented. If something breaks, you’ll know why.
The third is the master’s research translated to practice. What academic research has to say about the problems you face every day. No filter, no academic jargon.
Published in Portuguese and English, every week.
Hit reply and tell me: what’s the hardest data problem you’re dealing with right now? I read everything.
Thais Vaz