Data Engineering's Durable Skills: What AI Can't Automate
The data engineer who survives the next five years isn't the one who codes the fastest — it's the one who understands context. Here's what that means in practice.
The data engineer who survives the next five years isn't the one who codes the fastest — it's the one who understands context. Here's what that means in practice.
Ten quick questions on building retrieval-augmented systems that scale under load and don't leak data they shouldn't — access control, prompt injection, PII, evals, and caching. No reading required, just test yourself.
My chat bot confidently lied about my own writing. The bug wasn't retrieval - it was an ambiguous sentence in the source and a stale index. Why RAG is a data problem before it's a retrieval problem.
Ten questions on Beam's windowing methods, watermarks, triggers, and late data — with sourced explanations for every answer.
A short, high-level visual tour of the DAMA wheel: governance at the hub, the eleven knowledge areas around it, where data ethics fit, and my own attempt at slotting in AI.
We badly underestimate what we already know, about our craft and about ourselves. A Father's Day reflection on my daughters, journaling, and why writing it down is how you find out what's in your head.
You see a better way to build something, but you don't own the budget, the roadmap, or the teams. Six things that have actually worked for me to win people over and enact change.
Explainability has a fancy name, but it's built from things you already use - weighted scores, plain-English reasons, and an append-only audit trail. Here's a whole loan-approval workflow, walked through by persona.