Alan O’Shanghnessy
Hi, I’m Alan.
I’m a Data and AI Architect, but that title doesn’t quite explain how I got here or what I spend most of my time thinking about.
I grew up in Buenos Aires, Argentina, with a natural curiosity for how things work. Over time, that curiosity turned into a career designing data systems for large organisations. Today, I work in banking, where nothing is simple. Data is fragmented, systems are layered over decades, and every decision sits somewhere between innovation and regulation.
That tension is what pulled me into architecture.
I don’t just design data platforms. I spend a lot of time trying to make sense of how everything fits together. How data flows across domains. How decisions get made. Why systems that look good on paper struggle in reality. More recently, I have been focused on how AI fits into all of that without becoming just another disconnected layer.
A big part of my work is sitting in that uncomfortable middle space, between strategy and delivery, between governance and engineering, and between what companies want to do with data and AI and what their systems actually allow them to do.
In early 2025, I started a MicroMasters in Data Science and Statistics at MIT, as a way to deepen my understanding of the foundations behind the systems I work with every day. It has been a useful contrast. On one side, there are clean models and assumptions. On the other, there are legacy systems, trade-offs, and complexity. The gap between the two is where most of my thinking happens.
This blog is a reflection on my journey.
Sometimes it is about architecture decisions, how to design systems that scale, how to avoid common traps, and how to introduce AI without breaking everything else. Other times, it is more personal. What it feels like to navigate different tech cultures, or to sit in rooms where everyone is trying to figure out what AI actually means for their organisation.
I don’t have all the answers. Most people don’t.
But I have found that sharing the process, the thinking, the trade-offs, and the mistakes, is often more useful than pretending things are clearer than they are.
That is what you will find here.
Everything shared here reflects my personal perspective, not my employer’s.