Data & AI products,
end to end

I'm Debby

I build it, ship it, and make it stick

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About me

I spent years as a consultant telling stories with data...

Sketch of Debby: she builds it at her laptop, then sells it in the boardroom

...now, I engineer the systems that make those stories possible

A deliberate evolution. I spent the first half of my career mastering the why: running national surveys, leading the impact evaluation of a $580M social service fund, and winning an international research award. I spent the second half mastering the how: cloud data platforms, automated pipelines, and AI tools people actually use. Most people do one or the other. Doing both means I don’t just build the system. I build the right system.

I build the plumbing

Turning messy data swamps into trusted, single-source-of-truth systems. I’ve stood up AWS environments, automated ETL pipelines that replaced 2 hours of manual Excel pain with a 5-minute script, and maintained the certified data sources behind dashboards serving 1,300+ users.

I make AI prove it works

Problem first, technology second. Unlike traditional software, AI doesn’t scale for free. Every API call costs money. Every token adds up. So I think hard about cost structure from day one. I designed a Text2SQL engine that lets non-technical colleagues query databases in plain English. Not because LLMs are cool, but because the alternative was a 3-day turnaround from an analyst for every question. Shipping an AI feature is the easy part. Knowing when it’s wrong, and catching it before users do, is the work.

I translate for both the Boardroom and the terminal

A decade of consulting taught me that the best technical work is useless if nobody understands it. I’ve presented to 1,000 delegates at an international congress, trained teams on AWS and SQL, and built dashboards that drove 138% higher engagement than platform average. I’m equally comfortable in the terminal and the boardroom, and I know which room needs which language.

Awards

Highlighted Projects

Things I've Built

I believe the best way to learn is to do. A mix of production systems, competition winners, and weekend experiments. Some projects are anonymized for confidentiality but the engineering is real.

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Hiring, collaborating, or just curious?

I'm always up for a conversation about data, the systems behind it, or a problem you're stuck on.