Thirty years in Analytics and BI will teach you one thing above everything else: most problems that look like data problems aren’t.
Data Consigliere is where I work that out in public — documenting my move into hands-on Data Engineering and AI/ML. What works, what doesn’t, and what experience change about how you approach new technology.
No polished tutorials. No highlight reels. Just the real learning curve, written as I work through it.
The name is deliberate. A consigliere isn’t the one making the call — he’s the one making sure the right question gets asked before anyone does. Most projects don’t fail on execution. They fail because nobody wanted to slow down long enough to ask the uncomfortable question at the start. That’s most of the job, honestly.
Where to start
Recruiters and hiring managers — start with About. You’ll get the background, the approach, and a clear sense of how I think when there’s real ambiguity on the table. The writing will do the rest.
Business leaders — About first. Then find a post about a problem you’ve lived through. That’s usually where it clicks.
Fellow practitioners — head straight to Writings. Every piece has two layers: the technical and the contextual. You can read either one. The interesting part is where they rub against each other.
Everyone else — read one post end to end. You’ll know pretty quickly if this is for you.
What you’ll find
Three things, consistently:
Hands-on Data Engineering and AI — real implementations, real tradeoffs, real mistakes. The stuff that doesn’t make it into the conference talk.
A bias toward simplicity — not as an aesthetic, as a discipline. Unnecessary complexity is just deferred cost. Someone always pays it.
Experience as a lens — I’ve seen most of these patterns before, usually with different names. That’s not seniority. It’s pattern recognition.
Cadence
When it’s ready. Quality over schedule.
Writing here is also how I hold myself accountable. If I can’t explain it clearly, I don’t understand it well enough yet.