From Analytics Engineer
to AI Engineer
You already build production data pipelines and models.
Now learn to add the AI layer on top of your strong foundation.
You Model the Data.
Others Build What Uses It.
AI engineers earn 30-50% more while building on top of your data models.
Your dbt and SQL expertise is production ready, but ML feels like a separate world.
You are unsure which AI skills to prioritize given your analytics engineering background.
Your Data Modeling Skills Are the Foundation.
The World-Class AI Engineer Cohort
Analytics engineers have rare advantages in AI: you understand data quality, transformations, testing, and documentation. You already think in production terms. Learn to add embeddings, vector stores, and LLM integration to your existing dbt and SQL expertise.
Leverage Your Stack
Your dbt, SQL, and orchestration skills transfer directly
Add the AI Layer
Embeddings, vector databases, and LLM integration
Position Your Experience
Frame analytics engineering as AI infrastructure expertise
Meet Your Mentor
My aim has been the same for years: become a world-class AI engineer. Every career move I've made has been measured against that.
I started as a software tester on a $500/month internship in the Netherlands. Taught myself to code, learned to ship real systems, and worked my way to Senior Engineer at GitHub.
Then I left GitHub. I joined an AI research lab as Member of Technical Staff, where I currently build products for secure AI monitoring.
The cohort draws directly from my real experience so you can make progress fast.
I run this special cohort with only a few people because hands-on work with me is what it takes to bring you to become a world-class AI engineer.
Real Results
Vittor
AI Engineer
Built and deployed his portfolio piece, then landed the AI role
"The coaching played a huge part in my success. I focused on AI fundamentals, the certification path, and soft skills like professional writing. Having access to expert guidance gave me confidence during interviews and helped me feel I was on the right path.
I built my own platform (simple but functional) and deployed it on AWS. I used it in my portfolio and showcased it during interviews. The way complex topics were explained, especially the restaurant analogy for AI systems, really stuck with me. Focusing on doing the basics well was absolutely essential."
What You Will Get
8 Weekly Tuesday Sessions
3 hours each for 24 live hours total.
Project Scoping at Kickoff
We set the scope of what you'll ship and the milestones to get there before the live sessions start.
Code Reviews
Reviews of your code from Zen during the cohort.
Lifetime Demo Access
Every architecture demo is recorded and yours to keep.
Demo Day
You present what you built and get feedback from Zen, with a recording you can use in your portfolio.
12 Months Community Access
Included with the cohort.
Analytics Engineers Have Production Data Instincts
Frequently Asked Questions
Why are analytics engineers well positioned for AI roles?
Analytics engineers bring production data skills that AI teams desperately need. You already understand data quality, testing, documentation, and transformation pipelines. Your dbt experience means you think in modular, tested, version controlled code. Many ML engineers lack these fundamentals and struggle with data quality issues you solve daily. Your gap is narrower than pure data analysts: add embeddings, vector databases, and model serving to complete your AI toolkit.
What should analytics engineers learn for AI engineering?
Build on your strengths. Priority order: (1) Vector databases and embeddings, a natural extension of your data modeling skills. (2) Feature engineering pipelines, similar to your dbt transformations. (3) RAG systems, combining your SQL expertise with LLM capabilities. (4) Model serving basics, deploying models as APIs. Your orchestration experience with Airflow or Dagster transfers to ML pipelines. Focus on the data infrastructure side of AI systems where your skills shine.
How much time will this take?
You'll spend 3 hours every Tuesday in the live session and roughly 3 hours of async work in between, for 8 weeks. The Tuesday session time is fixed.
I've signed up for cohorts before and dropped out. How is this different?
It probably isn't, and you should hold the money. Most cohort dropouts are people who couldn't articulate what they were shipping when they signed up. That's why the consult exists, and why I turn down most applications. If we get on the call and you can't tell me what you'll have shipped at the end of week 8, I'll point you to the AI Native Engineer community until you can.
I'm not pivoting careers. I want to build a product. Does this still work?
Yes, the cohort works for people shipping their first serious AI system whether the goal is to land a senior role or to launch a product. The shipped system serves both equally well.
Do I need prior AI experience?
You need to be able to code in Python or TypeScript. Complete beginners can follow the classroom they get access to before the cohort sessions to come in well-prepared.
What does it cost?
It's a four-figure investment that we discuss during the 30-minute consult, alongside whether the cohort is the right fit for your project.
Can I do this while working full-time?
Yes, most attendees do. The live session is one Tuesday a week and the async work fits around your existing schedule, as long as you can carve out roughly 6 hours a week.
I accept those who have the highest chance of success.
In the 30-minute call we discuss your goals and whether you are ready for the program.