Industries:
Media agencies, e-commerce, marketing technology, and growth-stage companies where data sits at the center of client relationships or commercial decisions.
Outside of work I'm usually somewhere outdoors: snowboarding, mountain biking, or hiking. I write about cars and culture at TheGracefulApex.com.
Based between Florence and London on an EST schedule.
I've spent 10+ years at the intersection of marketing and data engineering; which turns out to be exactly where the AI readiness problem lives.
Most data consultants come from one direction: either they understand the business and marketing context but can't go deep on infrastructure, or they can build pipelines but don't understand what the data is actually supposed to do for a client-facing team. I've spent my career doing both.
That background—GA4 and marketing analytics on one side, data warehousing, pipeline architecture, and Python/SQL on the other—is what lets me walk into a broken data environment and understand not just what's technically wrong, but why it's causing pain for the people who depend on it.
Recently that work has converged around a single problem: companies are investing in AI tools and not getting the value they expected. In almost every case, the issue isn't the AI. It's the data foundation underneath it. I audit that foundation, fix what's broken, and get teams to a place where they can trust what they're seeing, before they try to put AI on top of it. For clients who need to go deeper, I also build attribution frameworks and LTV models. The analytical layer that sits on top of a clean data foundation and actually produces reliable answers.
What I work with: