The Dawn of the Data Intelligence Engineer
https://motherduck.com/blog/the-data-intelligence-engineer/I get that some specialisation is useful, but I would like to see more T-shaped data folks.
I think the specialisms are really helpful for hiring - data engineering and data science involve a lot of different things so it's a lot easier to hire someone if you don't require skill in both.
That said, I think we have a culture problem in defining our roles narrowly, I've seen data scientists look down at data-pipeline parts of their job, and ignoring model latency and deployment; data engineers be deeply uncurious around what happens to the data after they put it in a warehouse; etc.
The best people I've worked with, within data and elsewhere, are the ones who are curious about every aspect of the stack, even if they mostly focus on one side.
Then the BI people (who had never written a line of code except maybe some SQL) got jealous, started calling themselves "data engineers", broke everything ("what do you mean 'we need tests???'"), and ruined the title.
Like for example
>It doesn't know how your company counts revenue, or which of several similar-looking tables is the real one (it is revenue_cpx_new_revised_v2_use_this_one )
This assumption mostly only holds for non-clean data ( which is mostly a result of humans being lazy and backlog being clogged up ). As AI starts building more of the pipelines upstream - this becomes a non-issue.
Even for existing pipelines, I'd expect fixes to move upstream: when an agent encounters ambiguity at query time, the useful output isn't an edit of the context,but a PR that fixes it.
It's always been possible to massage your data to fit whatwver case you want to make. Previously you had to go through a data analyst and convince them to let go of their principles. Now you can just let an agent figure it out.
So how do you make sure insights are actually shareable (and trustable) with other parts of the company?
I think just exposing the metrics layer is probably the answer here.
It's kind of funny that the industry has tried to democratize data for so long, but now we scramble to restrict access to protect users from making incorrect decisions.
I made my 20 year career as a person who could do stuff with computers, software engineer etc. Every time I hear someone say, "my non tech friend who vibe coded an app got a bug he couldn't fix" etc, my response is they are using the wrong model and give it a few months. And I say this with great pain, I am in no way an AI booster, just a realist.
As long as we do not hand over our collective decision making power to the machine god, which I don't think is a good idea and am not sure is even possible, the world out there is at this moment still shaped by humans, with other humans, for humans.
I have this feeling that there is more about society and economics than efficiency. I have right now people I know that work in jobs that could have been automated for many years to a very high degree. That is even without AI.
Big companies though, are more concerned with value and risk. Say some data model provides $5 million in value, hiring a data person to check "everything is kosha", even if in theory someone could vibe code it by prompting an LLM, is a pretty good investment if it derisks the process.
What makes that quote so special that it is worth pointing out as "the best" in 11/2026?
I.e the reality has so far been ~2 years behind the hype.
I'm not saying I like these companies, or that I think the economics of any of them is sane. Just saying LLM's are capable.
edit. For some context, I only use open weight models these days and rather hope that big closed firms go bust.
They weaponize the HN rules here. Which is frustrating, because one has to break the letter to defend the spirit.
Umm, the model knows the same way humans know: either by looking into slack threads/confluence pages/emails… If your model has access to those things, then it can know. And I foresee that in the near future the model can simply have an incarnation as a Slack member, and when context is not enough, they will simply ask people around (and update docs accordingly)
Like a PT for AI's ... Getting them into shape, and fighting strong.
This fake job title sounds like what BI analysts or analytics engineers should be doing as part of their job, not a new job. These jobs are the ones that are usually working on the modeling, answering business questions, and making data accessible and would be creating the context, prompts, and evals to make sure it's working as intended. I think really, analytics engineering and BI should be combined.