In the previous issue, I shared some characteristics of analysts that will be thriving in 2026. This week we’re doubling down on a concept mentioned in that issue: Full-Stack Analytics (or Analytics/Data Engineering), a concept that has gained a lot of attention in the last year or so.

Let’s get right into it.

But first, have you taken this 30 seconds reader survey? It REALLY helps me out.

This Week’s Deep Dive: The Rise of the Full-Stack Analyst

If you look at most job boards today, you’ll see the same titles as the last decade:

Nothing unusual here. But if you look at the people who are actually making impact in companies, the ones driving strategy, leading initiatives, and being pulled into high-level conversations, they’re not behaving like traditional analysts.

They’re operating like something different.

⚡️ Enter the “Full-Stack Analyst” ⚡️

This type of analyst isn’t confined to dashboards. They’re end-to-end thinkers who solve problems across the business, and not just inside a specific few tools.

Now, you might be thinking “Dmitri, this is nothing new. It’s what all analysts do already, right?”. Well, kind of yes but mostly no. Traditional analysts do the same things, but with a much different approach, skillset, and most of all mindset. Let me elaborate a bit…

Being full-stack doesn’t mean:

❌ Becoming a software engineer
❌ Knowing every tool under the sun
❌ Taking on every role from data engineer to product owner

Instead, it’s about taking a problem from:

Vague → Clarified → Solved → Communicated

Not because you know everything, but because you understand how the pieces connect.

The Full-Stack mindset looks something like:

To be clear, Full-Stack is not a job description (at least not yet). It’s an analytics style. And it’s quickly becoming the competitive edge.

We can speculate this comes down to three forces:

Put all three together, and you get a new kind of analyst. One who’s multidisciplinary not by title, but by necessity.

Here’s a starter playbook you can apply immediately:

The Full Stack Loop

This loop shifts you from “task-taker” to “impact-maker”, and that is the essence of the full-stack approach.

In your next project or request, ask yourself:

“How can I take this one step further than what was asked? For my stakeholder, or for myself.”

Maybe it’s reframing the question. Maybe it’s packaging your findings with a recommendation. Maybe it’s using AI to accelerate the repetitive parts so you can free up your time. One small shift at a time, that’s how you start working full-stack.

P.S. - this does NOT mean add more work for yourself, it’s simply thinking about analytics in a new light. Optimizing and expanding your skills and workflow.

Alright, thanks for coming to my Ted Talk. Here are some interesting articles that goes into all of this some more…

Analytics Engineering vs. Data Engineering by Alex Dovenmuehle
This article explains how the modern data stack work is divided from Analytics Engineers to Data Engineers and how they work together. When executed well (aka “in a perfect world”), this reduces BI complexity, speeds decision-making, and scales analytics reliably. Read more

Data Analyst vs Data Engineer: The Key Differences by Integrate.io
More thoughts and perspective on this whole Data Analysts vs Data Engineers discourse. Integrate shares insightful information on both roles, their differences and how they overlap. Read more

Full-Stack Analytics: Benefits of Hybrid Approach to Business and System Analysis by Lex Melnykow
The business analytics market is expected to double in the next 9 years. This article ties in how System and Business Analytics ties into this and how full-stack analysts can leverage both to excel in the industry. Read more

SankeyMATIC #data-visualization #productivity
Obsessed with Sankey diagrams? Me too. Create custom diagrams with SankeyMATIC. A super easy web builder you can learn in under 1 minute. (I use this for my monthly budget)

Charticulator #data-visualization #productivity
A free, open-source, no-code tool developed by Microsoft Research that enables users to design bespoke and reusable data visualizations directly in the browser using drag-and-drop options.

Litmaps #productivity #research
It’s nearing finals time, and there's a great deal of students subscribed to this newsletter. This one's for you. Next time you need to write a paper check this website out. It will help you find credible, peer reviewed, real literature on whatever topic you are working on. I'm not being dramatic when I say it's saved me DOZENS of hours of research when I was in university.

▶️ Why You MUST Go All In on Data Analytics & AI in 2026 by Kedeisha Bryan
Kadisha highlights the urgent need to adapt your career by mastering the powerful combination of data analytics and AI, emphasizing that traditional coding roles are rapidly becoming obsolete due to automation and AI advancements. She explains that the future belongs to professionals who can interpret and communicate data insights while leveraging AI.

▶️ The Future of Data Analytics | 2026 Trends You NEED To Know by Jess Ramos
Jess breaks down the trends every analyst needs to focus on in 2026. There has been a shift in analytics for a couple years now and she hits the nail on the head with this one.

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Did you know? The majority of the world’s data has been created in just the last two years.