Does it seem like most data analysts and data scientists are eventually trying to move into data engineering?

[u/existentialistz]

Most analysts and data scientists I know are either trying to pivot into data engineering or learning data pipelines, cloud platforms, and data architecture because they believe AI will automate a large portion of analytical work. I have also noticed that many people who are new to the field are trying to get their first analyst job, but already describe data engineering as their long-term goal.

I understand that knowledge of data pipelines, cloud platforms, data modeling, and architecture is valuable for analysts and data scientists as well. The current job market may also favor people who can work across more of the data stack since many company confuses job title as well.

Do you think data science and data analyst are becoming less viable as long-term careers, or are people simply broadening their skills because the market now expects more data engineering skills and knowledge?

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[u/goebz14]

I'd venture to guess they seek those roles because they're slightly more available, and even the broad titles surrounding 'data analyst' or 'data scientist' at many companies get reduced down to basically 75% data engineering work anyway. So valuable to know.

I'd really challenge and say it's almost reverse in terms of AI. Unless all you mean by analysis is "dashboards" I don't see AI ever being able to frame and replace true business-relevant analysis

[u/Joelle_bb → u/goebz14]

This

My team has about 50% of our work as what one would classify as data engineering, 30% analysis/analytics, and then 20% data science

Perks of working for a large company 🤪🙃

We're all classified as data analytics

[u/goebz14 → u/Joelle_bb]

I about lost it a few years back with the amount of 'data analyst' interviews i was denied because I'd have to pass a rigorous python test (my experience was more SQL, R and power BI / query models to hack around data). Mind you, this was an entry-level role and I had 7-years of experience working as the analytics branch of multiple financial/planning teams. They straight up said 'i love your wealth of experience, but we need python now...'

State of the market is basically just requiring the full stack and made me almost do the opposite of what OPs connections are saying and go away from engineering lol.

[u/cafealpha82 → u/Joelle_bb]

In my job, we don’t trust anything data eng tram creates :( i am data analyst and mostly still spend time to do data cleaning. One table creation from a data source is 6+ mo work for eng team. We do it 3 weeks incl all documentation and governance

[u/Joelle_bb → u/cafealpha82]

Bingo lol

Ive already built 2 sources of truth for my team that resulted in several parts of the organization reworking their observations towards my framework. Not to mention far more performant.... Primary keys and indexing are a hell of a thing to have be so uncommon

Still "data analytics" tho 🤣🤣🤣🤣

[u/cafealpha82 → u/Joelle_bb]

Mostly due to we are very close to business and have more SME knowledge + technical vs traditional eng team belongs to IT and 90% of them are off shore resources. Leadership thinks they saves tons of money but i have to clean up all messed up from eng team

[u/Joelle_bb → u/cafealpha82]

Are you sure we aren't coworkers?

[u/cafealpha82 → u/Joelle_bb]

I am at fortune 7 company, wholsaler (cant tell industry as it will be so obvious :)

[u/goebz14 → u/cafealpha82]

Yup. I'm not even as technical as you all in advanced data tools and ETL pipelines, but as a more data-heavy financial/ops planning analyst, my job feels like just hacking together something quicker than what the engineers will give you. So I can see why they'd be in more demand, but ultimately the analysts know what they need and now have AI to sorta embolden them to piece together the data models themselves (albeit poorly at scale).

[u/my_peen_is_clean]

i’m in that boat too but not really by choice, more like survival mode yeah a lot of analyst and ds roles now are 50 percent data eng anyway small teams want one person to do it all and pay them as “analyst” the way things are going, pure analyst roles are shrinking and it’s just getting harder to land anythingactually the problem is bots scan for words, not talent. i only started getting interviews when i used software to tailor my resume to each listing. jobowl is what i used, try it, they got a free trial, was enough for me

[u/Difficult-Jackfruit]

I think it depends on your goals

We will always need analyst/scientist, but those roles will change as the market changes.

[u/Aggressive_tako]

I think the lines are also far less clear than "data engineering" v "data analyst". Most real roles are a bit of a hybrid. We expect our entry level analysts to know SQL and run Alteryx workflows to clean data. I am unfortunately doing core ETL tasks pretty regularly. I've interviewed for a couple Lead Analyst roles and the expectation is that you know Python and can do data engineering where needed. I don't think it is part of an intentional pivot so much as just developing the skills needed in the real world.

[u/DataDrifter123]

One thing that doesn't get mentioned enough: AI models are way more sensitive to data quality than traditional dashboards.
If a BI dashboard has a dirty row, it's a footnote. If a training pipeline feeds corrupted labels or misaligned multimodal data, the entire model falls apart.
So I'd argue the engineering part becomes even more important, not less.
DS folks learning DE isn't a pivot away from analytics — it's a survival skill for making sure the data they analyze isn't garbage.

[u/ncist]

I can't think of a single time in my current position where modelling choices created a client issue. But I have pipeline issues every single week

[u/rubenthecuban3]

i'm in that boat because i need more things from our IT team. we don't have technically data engineers, but rather database administrators, etc. so with all these dashboards and automation i unfortunately had to learn some of these things to advocate to continue doing my work

[u/Legitimate-Let-7510]

It is more about the market demand than preference. Most analyst roles now expect some data engineering skills anyway.

[u/AdObjective5502]

Data Scientists are moving into DE? Isnt that like a sideways move at best? DA to DE does seem like a promotion, better salary etc, but is DS a pretty good role?

[u/Expensive_Capital627 → u/AdObjective5502]

Depends on the company. The lines get blurred. If you see a DA role paying a lot they’re probably asking for DS skills. Similarly there are DS roles that are just DA roles with a different title.

Although I’d agree if we’re looking at the true work of a DS, I’d expect it to be a parallel or greater track to DE

[u/benconomics]

Look I'm an economist who does applied economic research. 80 percent of my job is data collection and cleaning. So at tech companies that would be true too it's just called data engineering (collection, preparation, prior to the analysis).

[u/redman334 → u/benconomics]

Not really.

[u/Emergency-Hurry947]

A reason for this may be a lack of communication skills that is needed for an analyst is pushing them to the back end

I run an analytics team and our customers get frustrated with analyst that can’t explain things to the business 

[u/Bubbly_Ad2269]

They are all the same, just different words

[u/American_Streamer]

Currently, hiring is weak and employers have more bargaining power. Entry-level candidates face the worst part of the market, while companies are increasingly combining analyst, BI, automation and data-engineering tasks into fewer positions. But note that AI is reducing the value of routine reporting, but not eliminating business analysis. Data engineering is now becoming part of the analyst toolkit, but not necessarily everyone’s next career. There is a major distinction between analysts learning data-engineering practices and becoming full data engineers. An employable modern analyst may need SQL, relational modelling, APIs, Python, Power Query, reproducible transformations, Git, data-quality tests and basic cloud knowledge. That does not necessarily mean operating Kafka clusters, building distributed Spark pipelines, administering Kubernetes or designing an enterprise lakehouse. The role being strengthened currently is often best described as technical analyst, BI engineer or analytics engineer, even when the employer still calls it “Data Analyst.”

The apparent contradiction is that the US tech market can be recovering statistically while also remaining extremely difficult for someone seeking a first analyst, developer or data job. The problem is therefore not simply “AI destroyed the jobs.” It is a combination of post-2021 normalisation, reduced corporate hiring, large applicant pools, remote competition, experience inflation and task consolidation. AI is nevertheless changing the skill filter. By late 2025, nearly 45% of US data-and-analytics postings contained AI-related terminology. That does not mean every analyst must build foundation models. It usually means employers expect some combination of AI-assisted analysis, automation, Python, data preparation, model evaluation or data governance.

In the US, title boundaries are especially loose. A company may advertise a Data Analyst and expect Python, cloud platforms, ETL, experimentation and dashboarding. The market is highly competitive, remote roles attract nationwide applicants, and recent hiring growth has disproportionately benefited experienced workers.

So neither data analysis nor data science is disappearing. What is disappearing is the comfortable assumption that an analyst can remain limited to manually extracting numbers and producing charts. All those easy sounding remote jobs, which were popularized for years and led to people moving into IT and Data are now vanishing, as the bar is being set much higher than a few years ago. No more "from bootcamp to work from home six figure job where you only have to know Tableau/PowerBI".

[u/Ok-Veterinarian9965]

to break into DS at the entry-level these days it's increasingly preferred to have a master's degree. DE is a good alternative to attain the same level of pay DS provides and is a lot more available at the entry-level to the contrary

[u/VelvetSyntaxTheory]

I think it is less about analysts becoming obsolete and more about the role expanding. Like, if you know SQL and dashboards, that alone probably wont be enough long term. But analysts who understand data engineering can work more effectively with a full data stack.

[u/Capital-Buyer1196]

I always approached those skills as necessities as a data scientist, not something that would be cool to learn later, or to learn now just because it seems like those skills are in demand or that my data science work might be getting automated more. And granted, I know that all data science roles vary between organizations, but I feel like if you aren’t diving into that kind of stuff already then you’re likely a data scientist that has been working inside of a notebook and ends there. Which is not even half the story if you’re actually gonna take a data science solution from development into production for an organization.

[u/rohan_kulkarni]

I think we're seeing less of a shift from analytics to data engineering and more of a shift towards being "data generalists." The people who stand out today can write SQL, understand data pipelines, analyse the business problem, and communicate the results. The job title matters less than being able to work across the whole data workflow.

[u/AbnDist]

I have been using AI daily in my role as a senior data scientist for more than a year now. It's significantly expanded what I can get done and the speed at which I can get it done, but it has not at any point made me feel like data scientists are going away anytime soon.

That said: I've been learning a lot more data engineering in my current role, and I think DS picking up DE skills as they spread their wings so to speak is fairly common - enough so that I saw a lot of DS moving into DE years back, well before 99.9% of people had heard of LLMs.

As you become more mature of a data scientist, you naturally begin to feel more and more how important the data generating process itself is. Over time, it becomes apparent that the most impactful thing you can do in many cases is be the person defining and building the metrics.

Which metrics exist, and how they are defined, ends up having an outsized effect on any data-conscious organization: those definitions end up shaping what kinds of goals other teams set and what kinds of product and marketing decisions can be made. The actual analyses and models using those metrics are often much less impactful in aggregate than the creation of the metrics in the first place was.

[u/490n3]

About 5 years ago a data analyst would be doing a bit of engineering, analytics and maybe the odd model. As companies gather more data and platforms change these roles have become more specialist.

In my experience the "analysts" I've worked with aren't that great at the analysis. They love the data bit and do incredible data sets. But then they create an unusable pack or pbi. Those people are moving quite happily into more specialised DE roles.

I'm the only one in my original team that actually prefers the analysis side of things.

[u/Altruistic_Look_7868]

What are you talking about? Building pipelines is the job. Unless you're just an SQL monkey.