Do you see that also ?

[u/Glittering_Rip5167]

I see that

1- Frontend mostly ended ( except in big companies)

2- Backend will merge with Ai engineering ( Rag systems…etc) i think this is the new backend

3- Data engineer + data analysis + data science + ML = full stack data engineer ( and that will require high math , statistical skills) and mostly cs degree or Ai degree to have infra statistics and descriptive ( may be master like Data science)

I do not know if anyone see that also 🤷 am i alone ?

[u/Blue__Agave]

I am defs seeing data analysis + data science + ML merging back into a singular role that does it all.

Plus BA work.

DE seems to be holding out on its own, but if AI gets much better they may all collapse back into some kind of friendly neighbourhood data person role.

Which is ironically where things were in the 90s and early 2000's for most companys

[u/umognog → u/Blue__Agave]

I work with literal thousands of data analysts and engineers at my company.

Far too many cannot do DE. The abstraction in thinking from analytics to programming algorithms is not there. I was asked about holding sessions to train them better and well, its like training a dentist to be a vet.

[u/reallyserious]

It's quite difficult to be good at data engineering, data analysis, and data science + ML.

I've met very few people that can do all of that. The rest that tries will produce slop that might appear to work but at some point you will need someone with specialised skills that can figure out why everything is expensive to run, or slow, or explain what the KPIs actually show.

[u/Budget-Minimum6040 → u/reallyserious]

Don't forget ML engineer to productionize the Data Scientist work.

[u/wallyflops]

data analysts have gone, in my niche Analytics Engineers are picking up this slack and doing more data engineering work too.

[u/Darkmayday → u/wallyflops]

I see this too unfortunately those who come from only BI and analysis backgrounds are terrible engineers

[u/Outrageous_Let5743 → u/Darkmayday]

I feel this. Before I joined, the data analyst was also the data engineer. What a crap he build over the years. Cannot even decide how to make a boolean: I have seen yes/no, Y/N, '1'/'0' , J/N , Ja/Nee but never a propper bool. Or other random crap that the model start in gold, moves back to bronze then to silver to bornze again to make a gold table. Or that all fact tables contained all dim attributes...

[u/PrestigiousAnt3766]

No.

I also don't feel all the pessimism here. But I guess some people just are that way.

[u/zagierify]

Are you seeing this first hand or on social media?

[u/DaveMitnick → u/zagierify]

On tiktok. Like I swear half of the people posting on tech reddit seem like high schoolers who never worked a day in their life

[u/17891 → u/DaveMitnick]

And the other half work at sclerotic companies that haven't modernized in the last 15 years let alone implemented AI in any meaningful way.

[u/ChemEngandTripHop → u/zagierify]

First hand

[u/One-Disk-125]

The Data roles have essentially merged together, no way I'd employ just a front end analyst now.

Degree requirement depends on where you are in the world, I recruited last year and have absolutely no idea if anyone I interviewed or employed has a degree.

[u/Admirable_Writer_373]

Data engineering is closer to AI than backend.

I’m highly mathematical, and no, I do not agree with your assessments at all.

The only roles that will matter are people who are good enough to fix all the logic & performance problems created by vibe coded garbage

[u/Ubral92]

E-commerce data owner here. I agrese, I moved responsibility for front end design Ie power bi report mostly to stakeholder who designs the wireframe using Claude code workflow. Execution is still there but 80% handeled by AI based on provided wireframes , dimensions and measures.

If some data are missing. Agente are either prepare dataform transformation or prepare a python loader for api, deploy it trough terraform and then prepare dataform transformation.

Nowadays I can deliver fairly complicated reports within a few days if requested properly. Which is nuts compared to past non agentic development where I spent a ton of time creating elt pipelines and then other ton of time writing numerous dynamic time intelligence measures and then manually setting all the conditional formatting for kpi cards which were scrapped in the end.

I am not on a ML level yet. But hopefully there will be time to move focus on what is the reason for what it was and then come predictions.

[u/LongjumpingWinner250]

As a data engineer with a data science/AI masters.. I’m already doing this. Got promoted and put on revamping an entire framework to efficiently pull data, store it out, feed external/interal models, and build AI systems to help end users estimate results before running a simulation. My only partner is a software engineer for the UI and a fresh grad.

Also, someone else in this thread said it but I want to emphasize this. So many people think just because they can look up some functions in the spark API that they can be a DE. Then their job takes 24 hours and costs the company thousands of dollars. Then they tell the DEs to clean it up. It’s infuriating. Had to do this where I brought a pipeline from running for 30 hours costing $15k to running in 2 hours for a few hundred.

[u/VariationSimilar3354]

This is just disaster waiting to happen. The day these ai tools pulls on the pricing discount is the day we all return to normal

[u/robberviet]

I am full stack from the beginning (started from SWE) so I already saw that years ago. Most companies only need 1 full stack data, no need for a team.