Modern Data & AI Engineering
The models are commoditized — everyone has the same ones. The advantage, and the risk, now live in the data, the platform, and the governance underneath them. That layer is exactly what I've spent 25 years building. When AI stalls, this is almost always why.
The short version: AI didn't retire the data problem. It raised the stakes on every part of it — and made the foundation the thing that decides whether AI ships or stalls.
Start with your problem
Most AI problems turn out to be data problems wearing a disguise. Find the one that sounds like you — each goes straight to the right conversation.
Why AI stalls
The failures making headlines aren't model failures. They're foundation failures — and they're expensive.
For twenty-five years the job was the same: get clean, trusted, well-governed data to the people making decisions. AI didn't retire that job — it raised the stakes on every part of it. When an AI initiative stalls, the model is almost never the reason. It's the foundation underneath: data that's incomplete, ungoverned, undocumented, or trapped in formats the AI can't read. The model is the easy part now. The foundation is the whole game.
That's the hard news and the good news at once. The differentiator is no longer access to AI — everyone has the same models. It's whether your data, platform, and governance are ready to feed them. Get that right and AI compounds; get it wrong and you're funding pilots that never ship. Building that foundation for some of the most demanding enterprises in the world is exactly what I've done for 25 years — now aimed squarely at making AI real instead of theoretical.
What changed
The fundamentals didn't disappear — they got harder, and they got more valuable. Here's what actually shifted, and what it means for the foundation you build.
Data that was good enough for a dashboard often isn't good enough for AI. AI needs context, not just columns — rich metadata, clear ownership, lineage, and business meaning that a machine can read on its own. Traditional data management gets you partway; closing the gap is what separates AI that works from AI that's abandoned.
Ask AI a question against raw tables and it guesses — often wrongly. Ground it in a governed semantic layer, where every metric is defined once and consumed everywhere, and answers become trustworthy. Studies put AI query accuracy in the low 40s without it and above 80% with it. This is now foundational infrastructure, not a nicety.
Roughly 80–90% of what an organization knows lives in documents, contracts, emails, and transcripts — and historically almost none of it reached analytics. AI changes that: for the first time, that knowledge is usable. Making it accessible, governed, and safe to query is a core new part of the engineering job.
AI moves sensitive data faster and further than anything before it — shadow AI, leakage, and ungoverned access are now board-level risks. But governance isn't what slows you down; done right, it's what lets you move fast without fear. Lineage, access control, and observability are the seatbelts that make speed safe.
How I help
Bring me where you're stuck — deciding what to build, building the foundation itself, or getting your team working AI-native. Most engagements start with one and naturally expand.
What's AI-ready, what isn't, what to fix first.
A clear-eyed read on your data, platform, and governance — and a sequenced plan to make them AI-ready without boiling the ocean. Architecture and platform decisions, governance design, and the judgment to tell what matters from what's hype.
I own the outcome — hands-on where it counts, directing where it scales.
Modern platform, governed pipelines, a trusted semantic layer, and unstructured data made ready for AI. I architect it and own delivery — building hands-on where it counts, directing your team or a vendor where it scales — so it ships without cutting corners on quality or security. A working foundation, not a deck describing one.
Productivity that outlives the engagement.
Your engineers spend most of their time on repetitive build-and-maintain work. I coach teams to use AI to generate pipelines, tests, and documentation — safely and to a standard — so the capability sticks after I'm gone.
You get one senior expert, accountable end to end — not an agency, not a bench of juniors, and not a vendor you have to manage. When the work needs more hands, I bring in and coordinate the right resources. You keep one point of accountability: me.
In practice
Not theory — engagements where modern data and AI engineering are in production. This is where I've already been, not where I'm headed.
Migrated 300+ legacy analytics applications into a governed Databricks ecosystem, applying AI to convert and remediate code at enterprise scale — modernizing the foundation on aggressive timelines without breaking what the business runs on.
Built a GPT solution to automate post-upgrade code conversion inside a validated pharmaceutical environment — proving AI-native delivery can meet the strictest compliance bar — then coached the analytics team to lift quality and productivity with Copilot.
Introduced natural-language query for self-service analytics and applied decision-tree modeling to surface the real drivers behind sales performance — putting AI to work on the questions leaders actually ask, grounded in governed data.
Supporting a full application rebuild using Claude Code and modern tooling — applying AI-native engineering to ship a maintainable codebase faster than traditional methods allow, without cutting corners on quality or security.
Whether your AI is stalling, your data's a mess, or you're modernizing for what's next — start with one conversation and I'll map a confident path from foundation to working AI.
Start the conversation