HeyJaredJody
Data Engineer 📊 Independent Consultant 🧭 Mental Health Advocate 🧠
- Indexed issues, last 90 days
- 4
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- Jul 29, 2026
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- Jul 9, 2026
Latest issues
The Proof is in the Pipeline (Part 2) (opens the original)
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All the talk of gradient descent and matrix multiplication that has come with the advent of LLMs really warms my nerdy little math heart. But beyond LLMs (and AI/ML more generally), and as I’ve mentioned before, there’s a surprising amount of math that comes in handy as a Data Engineer despite how much is abstracted via the declarative logic of most modern data processing languages (SQL, PySpark, etc.). I thought I’d take a moment to walk through one such example as the second article in my seri
A Technology Leader's Guide to Hiring Consulting Firms and Freelancers (opens the original)
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Last year, was kind enough to host me for a talk to technology leaders on hiring consulting firms, where I covered three things:when it makes sense to hire consulting firms,how to find strong consulting partners, andhow to maximize value from consulting engagements.In the following article, I’ll capture that same content: how leaders can most effectively hire external consulting firms for their technology needs, drawing on my experience partnering with large firms (Deloitte, EY, Accenture, CapGe
Fundamentals of Engineering: Part 2 (opens the original)
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In my previous article, I highlighted a problem related to the engineering capability in growing orgs, that can more meaningfully be considered as two separate problems:1) Engineering leaders often don’t have a shared language, beyond their own technical sub-discipline, that they can effectively use to communicate with business stakeholders, and 2) Quickly growing orgs and teams, that find themselves having to deploy enterprise technology (digital or physical) in order to grow and scale, do not
Fundamentals of Engineering: Part 1 (opens the original)
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A lot of technical leaders and peers I socialize with often emphasize the importance of understanding the fundamentals of a given engineering discipline (especially data engineering and software engineering), and rightly so. Why? Because the inverse - emphasizing technology and tools over the fundamentals of software or data engineering itself - leads to all kinds of problems in architecture, quality, development processes, and ultimately value realization. However, one major gap in the discussi
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