The AI-Fluency Career Kit for Data Engineers

Everyone on your LinkedIn feed suddenly calls themselves AI-fluent. You use AI tools sometimes. You don't know if that counts, and you don't know who's actually hiring for it.

This is the premium kit for Data Engineers who've already decided AI-fluency positioning matters (maybe you took the Market Clarity Kit's Decision Framework and landed on "upskill and reposition") and now need to do it right: honestly, with evidence, aimed at companies that are actually hiring for it.

What's inside:

1. The AI-Fluency Self-Assessment Quiz. 20 questions, scored the way an interviewer would score you, not the way LinkedIn confidence would. Get your real tier before you claim anything publicly.

2. AI-Fluency Positioning Kit 2026. The exact language that survives a follow-up question, a swap table replacing buzzword phrases with specific, credible ones, and a "Prove It" mini-portfolio method using work you've already done.

3. The Production Proof Portfolio. Talk is coachable, artifacts aren't. Three artifact types (architecture diagram, before/after metric writeup, incident postmortem) that make skeptical interviewers believe you, not just listen to you.

4. The DE Hiring Signal Report 2026. A system for reading which of your target companies are genuinely expanding data/AI roles versus quietly relabeling the same headcount, so you stop applying blind.

Who this is for:

• Data Engineer, 4-8 YOE, ready to reposition around real AI tool usage
• Already have some genuine usage, want credible language and proof, not a crash course
• Want to target companies with real signal, not just apply everywhere

Who it's not for:

• You haven't used AI tools in your actual work yet, build real usage first
• You want to learn AI/ML from scratch, this is positioning, not a technical curriculum
• You just want the market overview, start with the Market Clarity Kit instead

Why this, not a generic AI course: the credibility scoring, the artifact types that move a room, and the company-signal system all come from screening hundreds of these claims a year and watching what survives a live follow-up question. That calibration doesn't exist anywhere else.