Our analytics engineers can extract their own data sources now. A year ago I wouldn’t have wanted them to.
Not because they couldn’t write the code. Doing extraction properly meant knowing too many things outside their normal area: authentication, pagination, memory, schema changes, retries, monitoring.
So we built the framework instead. It runs on dlt, and most of that knowledge sits in the framework now instead of in a person.
We’ve been doing the same in analytics engineering. Our standards and methodologies increasingly live in skills the agents follow, so analysts and data engineers can contribute to the same codebase safely.
Same with our semantic layer. The approach we take is in a skill, and we even have one that walks you through whether you need a new topic or just some smaller changes.
None of it is airtight. “Safely” is carrying a lot of weight in those sentences. Someone still reads everything before it lands, and that part hasn’t got cheaper.
We originally built these frameworks to make our own work easier and more maintainable. But with AI, they started doing something more interesting: raising the floor for everyone else.
The boundary moved from both sides
We use Omni. Our stakeholders ask questions in natural language against our governed semantics, or build dashboards themselves on the same models our analysts use.
The quality varies, of course, and a lot of what they build is quite niche. But it changes the role of a data analyst.
Many analysts I’ve known have mostly been building dashboards. Not going deep into figuring out how to change the processes of their stakeholders. That work isn’t going away. It’s just not the scarce part anymore.
When your stakeholders can do all the basics themselves, you need to go further into their area to provide more value. Understand how the process actually works. Know which questions matter before anyone brings you one.
Which was arguably the job all along. Some analysts just got stuck building dashboards.
Execution alone is not enough
I don’t think this is specific to data. As AI makes more of the basic execution cheap, I see three directions for knowledge work. I see them most clearly in data, because that’s where I’ve been doing a lot of the thinking, and I can watch the bottlenecks move.
Breadth. Do more of the adjacent work yourself. An analytics engineer setting up extractions. A data engineer building entity models. An analyst updating governed semantics. You’re still deep somewhere. You have to be. But the threshold for working in the areas around you gets lower.
Depth. Understand the domain much more deeply. Waiting for stakeholders to bring you context isn’t enough. You need to understand their world well enough to form your opinion of what matters.
Leverage. Raise the floor so other people can do more. Turn what you know into frameworks, skills, standards and systems that let other people do work that previously needed you.
This might sound like T-shaped people. I think it’s different.
The horizontal part of the T was mostly about knowing enough to work with other disciplines. Breadth here means actually doing some of that work. And depth here isn’t deeper expertise in your own discipline. It’s deeper understanding of the domain you’re applying it to.
The people I find most valuable increasingly combine these.
Breadth and depth: understand the problem deeply and build most of what it takes to solve it.
Breadth and leverage: work across the stack and build things that make everyone else better.
Depth and leverage: understand a domain well enough to encode how it works into semantics, workflows and systems other people can use.
We’re looking for a process automation engineer this summer. Writing the role, it became obvious the person we really wanted needed all three: enough depth to understand how a finance process actually runs, enough breadth to work across the systems involved, and enough leverage to turn the solution into something reusable.
I don’t think everyone needs all three. Expertise still matters, and business users can’t suddenly extract data sources or build reliable data models because they installed Claude Code.
But the boundaries are getting blurrier.
The people I’d bet on are getting wider, going deeper, or raising the floor for everyone else.
Increasingly, two out of three.