The Challenges of Driving a Data-Driven Culture
What I learned trying to move 574 people toward data fluency — why building great data infrastructure is the easy part, and why the hardest problems have nothing to do with code.
Data & Analytics Engineer who builds pipelines, data models, visualizations, and insights that matter — and occasionally analyzes the emotional arcs of Studio Ghibli films.
I'm Eduardo Franklin — a Data & Analytics Engineer with 10+ years building systems that turn messy data into clear decisions.
My decade at MercadoLibre taught me that great data engineering isn't just about pipelines — it's about understanding the business problem first, then building the simplest solution that scales.
Currently, after working with seller invoices, logistics, and loyalty teams across multiple projects, I'm helping the Tech Corporate business unit build a strong data foundation from a legacy sandbox after years of rapid growth in capex and opex.
When I'm not working, I'm practicing calisthenics, climbing, hiking, biking, or eating sweet treats with my elder dog.
The emotions of Studio Ghibli, decoded
A data engineering passion project that analyzes the emotional arcs of Studio Ghibli films using NLP and machine learning.
From My Neighbor Totoro's gentle wonder to Princess Mononoke's fierce beauty — every film tells an emotional story that data can reveal.
Technical deep-dives and project breakdowns
What I learned trying to move 574 people toward data fluency — why building great data infrastructure is the easy part, and why the hardest problems have nothing to do with code.
A retrospective on building a full Data Mesh environment at MercadoLibre's Internal Systems, migrating 6+ pipelines to production, and planting the seeds of a data-driven culture initiative.
Why I chose a 10-minute rolling window for emotion smoothing — and the trade-offs between noise reduction and temporal precision