Upskilling a Data Team on AI Tools That Actually Stick
Why most AI training doesn’t change behavior—and a field-tested format that does. Includes prompt patterns, SQL review norms, and a 90‑day automation plan.
6 min read
Analytics engineering
Analytics engineering practice
How modern data teams are structured, staffed, and run — and the habits that separate the fast ones.
Data Team Structure: Who to Hire First, Second, and Third
Centralized vs embedded vs hub-and-spoke, with clear 'pick X if' guidance and the first three hires to make. Practical org patterns from production teams.
9 min read
dbt Mesh: When to Split One Project into Many
When should you split one dbt repo into many? Concrete signals, what breaks in production, and a safe migration path—plus CI, contracts, and ownership.
7 min read
Business Health Dashboard Leaders Actually Open
A practitioner’s plan to build a business health dashboard that leadership actually uses—metrics, piping, dbt models, BI choices, weekly ritual, and failure modes.
7 min read
AI for Data Teams: Workflows That Actually Stick
Which AI workflows actually last past the pilot for a data team? A skeptical, side‑by‑side comparison with code, failure modes, and metrics.
7 min read
Modern Data Stack for Startups: What You Need First
Stage-appropriate picks, plain-English tradeoffs, and real signals for when to add each layer of a modern data stack—without overspending or overbuilding.
10 min read
Analytics Engineering Consultant: What to Ask Before You Hire
A practitioner’s buyer’s guide to hiring an analytics engineering consultant. Learn when to use a consultant vs FTE, how to scope work, evaluate candidates, spot red flags, set timelines, and structure handoff so your team owns the result.
9 min read
Refactoring Legacy SQL in a dbt Project—Without Breaking Reports
Inherited a 600-model repo nobody understands? Here’s the operator’s playbook to refactor legacy SQL in dbt safely, prove parity, and keep BI stable.
4 min read
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