Field notes on data & AI — from 28 years in the trenches.
Production systems at petabyte scale. LLM agents in data pipelines. Platform design decisions that age well. Written by a senior data engineer in Rio de Janeiro for engineering leaders and builders 10+ years in.
I Integrated AI Agents Into Our Data Pipelines. Here's What Actually Worked.
The agents were good. But they hit a ceiling I didn't expect — and the thing that broke through it wasn't a better model. It was a knowledge base.
MCP, Agentic Workflows, and Guardrails: A Production Field Guide
What Model Context Protocol actually looks like in a real engineering workflow — how to set up context servers, build guardrails that earn trust, and integrate agents where they matter.
The Medallion Architecture Is Not What You Think It Is
Everyone talks about bronze/silver/gold layers. Most implementations miss the point entirely. Here's what the pattern is actually for — and when to break the rules.
Building a Data Platform From Zero: A Field Guide
What I learned designing and building a complete data platform for a growing company — from first table to production analytics. The decisions that matter, the order that works, and the mistakes everyone makes.
How I Reduced a Petabyte Pipeline's Cost by 40%
Performance engineering at scale isn't about clever tricks — it's about understanding economics. A case study in Spark optimization, partition strategy, and thinking in dollars instead of milliseconds.
The 70% Bug Squash: What One Hackathon Taught Me About Tech Debt
How a 2-day hackathon at a Brazilian e-commerce company squashed 70% of outstanding bugs and improved uptime from 92% to 99%. The real lesson isn't about hackathons — it's about permission.
From 1.5 Billion Records/Day to AI Agents: My 28-Year Engineering Arc
From telecoms in 1998 to AI-augmented data engineering in 2026. What stayed the same, what changed completely, and what I wish I'd known earlier.