My professional life has been long, from restaurants to call centers, however my corporate life started at Amazon in operations, the kind of work related to packages and fulfillment centers. Eventually, I moved into Business Intelligence Engineer in Luxembourg, which was amazing. My team was quite strong in data engineering. They breathed pipelines and Redshift. I had to adapt and I learned a lot.
Also, I had an amazing opportunity to build some web applications:
1. A system to transform and format text into HTML, this is widely used at Amazon
2. A full aplication to automate compliance management for Anti-Money Laundering and Suspicious Activity Monitoring.
I work as Data Engineer on a small company.
I've also built full-stack applications—backends, frontends, UIs, the whole stack—and regular websites when the project calls for it.
Honestly the best moment in programming is when this thing that you are working on finally works after hours of not working.
Also, sometimes I do some vibe coding. Just testing a fresh idea, proving a concept works, no pressure.
I also offer consulting and project services in AI and automation.
Cloud & Infrastructure
AWS is my home base. My favorite service? Lambda. Here's why: most courses teach you how to program, but they never teach you how to execute your code somewhere other than your laptop.
Lambda changed how I think about deploying scriptss.(I also like docker)
I've also spent serious time with enormous Redshift clusters, S3 data lakes, Glue, Step Functions, and pretty much every data service AWS offers.
Data Engineering
Full-Stack Development
AI & Agentic Workflows
I'm currently getting deep into agentic workflows and AI applications—specifically RAG systems and frameworks like LangChain. The intersection of data engineering and AI feels like where things are heading, and I want to be there as it unfolds.
I love open source. You don't need to reinvent the wheel for every project. Most of the time, there's already a solid open-source solution out there—you just need to adapt it, host it yourself, and make it yours. This approach has saved me countless hours.
I'm also a Professor of Data Science and AI at Nuclio Digital School. My teaching philosophy is simple: learn by doing. You can read all the theory you want, but until you've built something, broken it, and fixed it, you haven't really learned it.
If you're working on something interesting—data pipelines, AI experiments, scaling challenges—I'd love to hear about it. I really enjoy meeting people who are passionate about this stuff and exchanging ideas or thoughts.
A RAG (Retrieval Augmented Generation) system to chat with your documents. GitHub Repository
You'll find me outdoors. Hiking, running, playing padel, biking, climbing anything that gets me moving and away from a screen.
I'm always open to connecting and exchanging ideas. Whether you're working on something technical, exploring new concepts, or just want to talk about data at scale—reach out.
Building with data. Learning in public. Teaching by doing.