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Breaking into data feels harder than ever right now. I break down the real trends shaping the data job market in 2026 and what they mean for your career.
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Special thanks to Live Data Technologies for the data.Learn more about them: https://www.livedatatechnologies.com
⌚ TIMESTAMPS
00:00 – The real state of the data job market in 2026
02:18 – Why data engineering keeps growing while other roles slow down
05:41 – Who is actually hiring data analysts right now
07:56 – Why big tech layoffs don’t tell the full story
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Transcripts
Avery Smith-1: If you're breaking
into data analytics right now, you're
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probably pretty depressed and pretty
anxious with everything that's going on.
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00:00:05,640 --> 00:00:07,680
It feels like there's no data jobs left.
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00:00:07,710 --> 00:00:10,800
The few data jobs that are left are
uber competitive, and the rest of
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00:00:10,800 --> 00:00:14,190
the data world is just going to be
replaced by AI by the end of the year.
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If you feel this way, I don't blame you.
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It's super easy to fail this way in
today's market, but I'm going to share
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some raw, transparent numbers that
I think is gonna give you a little
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bit of hope and a little bit more
insights to what the data market is.
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Actually like right now and what
you can expect moving forward.
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The first question you probably
have is, are data rolls die?
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And the answer is no, but a lot
of them aren't growing either.
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Let me explain.
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So this chart right here shows the growth
of data rolls over the last four years.
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You see that data engineer
roles have grown 49%.
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Data analyst roles have grown about 12.6%,
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and data scientists
have grown about 11.7%.
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What this basically means is if there was
a hundred data engineers, data analysts
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and data scientists, at the end of 2021,
there is now 149 data engineers, 113
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data analysts and 112 data scientists.
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And so your first thought might
be, wow, look at data engineers.
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Like they have grown a lot and the
answer is yeah, they've grown a ton.
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And one of the reasons why is ai, AI is
only as good as the data you feed it.
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And data engineers are really good at
storing big data and cleaning big data.
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And that's exactly the type of
things that AI companies need
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to actually make useful models.
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So that's one of the reasons why we see
a really big growth of data engineers.
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The other reason I think we're
seeing a big growth of data
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engineers is we overhyped data
analysts and data scientists.
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The data scientists role was actually
voted the most sexy data title of.
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The 21st century.
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And the answer is, data science is
really cool, but it, once again, if
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you don't have really structured,
really clean, well stored data,
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you can't actually do that much.
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And so data engineers basically
didn't exist 10 years ago really, as
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at least the way that they do today.
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And so we were trying to do all
these really cool data analytics
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and data science projects
without proper data engineering.
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And that led to a lot of data
science projects failing.
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Now we're kind of going backwards
as a society and being like,
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okay, we need data engineering.
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We need data engineers to build the
fundamentals of a good foundation that
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we can actually build our data analytics
and data science projects on top of.
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So I think we had a little bit of
false, like mega growth for data science
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and analytics in the past decade.
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And now data engineering is
just kind of catching up.
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Now.
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It is important to actually
realize this is growth.
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This is actually raw numbers.
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So right now there's actually.
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51,000 open data analyst jobs
on LinkedIn across the world.
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And there's only 25,000 open
data engineer jobs and even less
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13,000 open data scientist jobs.
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So although data engineer has grown
quite a bit in the last four years,
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it's still nowhere as large as the
number of data analyst jobs that
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are open in the world right now.
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Now let's talk about the data
analyst and the data scientist
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role over the last four years.
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Growth has kind of become
stagnant in the last two years.
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Now why is that?
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There's lots of options.
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You could argue that AI is the reason, but
for me, once again, I think companies are
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investing in their data organizations, but
specifically they're putting an emphasis
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on the data engineering because they
know if they get the data engineering
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right, and they do that well, the data
analytics and data science teams and
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projects will kind of follow after that.
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Also, I think it's important to
realize that there's a lot of
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pressures going on in the world.
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Specifically what I know is the us
there's like a crazy political thing
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going on where there's tariffs.
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No, there's not tariffs,
there's visas, there's no visas.
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Like the stock market is
up and down every day.
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Like things are a little bit tight.
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It feels like we're gonna have
a recession or a financial crash
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soon, but it hasn't happened.
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I've been waiting years for it to
dip down, so I could buy the dip,
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but it just keeps going up and up.
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So I think to stay stagnant
isn't actually necessarily bad.
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There's not less data jobs, it's just
like we're in a weird place where
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we're trying to see what's happening.
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Now personally, if I'm being
100% honest and transparent.
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I really see this trend continuing
through the rest of this year.
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For the most part, I think a lot of
companies will still put most of their
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investment into data engineering to
try to get that sound foundation.
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Although I could see a lot of
companies have already done that, and
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so you might see a slight uptick in
data analysts just because there's
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quick wins for data analysts to
have once that foundation is laid.
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By the way, this is the type of analysis
graphs and data that I try to share
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every week in my newsletter that's
specifically for data professionals.
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It's a hundred percent
free and it's 25,000.
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00:04:06,960 --> 00:04:10,410
Other aspiring data professionals have
already joined, so why not join them?
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Go to data career jumpstart.com/newsletter
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or click the link, the
description down below.
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The next question you might be asking
is, well, there's no data jobs left.
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What companies are even
still hiring data analysts?
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And you might think it's the FANG
companies, but really it's not.
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So who are the companies
hiring the most data analysts?
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Well, this chart right here basically
shows you the top 20 companies that
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hired data analysts in the previous year.
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And number one, we have
Accenture two, Amazon three,
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McKenzie four, Deloitte, five C.
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Six American Express seven, capital
one eight Tata Consultancy, uh, nine
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Cognizant and 10, uh, TD Ameritrade.
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You can read the rest
of the list down below.
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Now, if you really analyze this list,
what you'll notice is most of these
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companies are either consulting companies
or financial service companies and bank.
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And that's really important to realize
because a lot of people think in
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order to work for data companies,
you have to work for like Microsoft
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or like Google or like Apple, and
that's really just not the truth.
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Obviously the tech companies are really
cool and they have cool products,
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but there's so many companies.
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Basically every company needs.
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Data people, they need people to
look at the numbers to actually make
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data-driven decisions for their business,
whether they're a hospital or a bank,
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or even like a mom and pop shop, like
data analysts are needed everywhere.
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So although you might really wanna
work for a tech company, and tech
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companies are cool, just remember
there's so many other options out there.
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And my suggestion is really to probably
focus on these consulting and financial
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service companies because these are
the people who are looking like they're
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dedicated to paying and hiring data
professionals moving forward in this
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challenging, tight economic time.
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And at this point you might be
wondering, well, Avery, where
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did you get all of this data?
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Like is it even valid?
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And the answer is, I got it from a
company called Live Data Technologies.
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They're a data product, and if you listen
to an episode that I released recently,
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or you're subscribed to my newsletter,
you learned what a data product is.
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But basically they sell data as a service
and they're tracking working professionals
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in real time so that you can actually
see where people are going, how companies
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are shifting, what roles are going up,
what roles are going down, what companies
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are actually hiring, what companies
are firing, those types of things.
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And they were actually kind enough
to send this data to me and let
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me share it with all of you.
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So if you wanna learn more about
them, you can check their link
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in the show notes down below.
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Next, I have a lot of people come up to me
on LinkedIn or in person and they'll say,
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Hey, Avery, tech roles, they're cooked,
meta just laid off this many people.
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Intel just did 20,000 layoffs,
like with no warning whatsoever.
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It's over for data jobs,
it's over for tech jobs.
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But here's the truth, you might be
missing once again, the big F companies.
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They dominate the headlines.
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Yes, they're the biggest companies
and maybe they're the most
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important to the US economy.
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Sure.
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But there's still thousands of
other companies who are hiring
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data people all the time who maybe
aren't laying anyone off right now
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who are maybe hiring right now.
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And to illustrate this, I'd
like to actually share a
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personal story of layoffs that.
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Completely affected my life
and is probably the reason
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I'm here talking to you.
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This chart right here is comparing and
contrasting the stock price of ExxonMobil
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to the stock price of meta or the stock
price of Facebook from 2019 to 2021.
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And the reason I'm showing you this
is at the time I was actually employed
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at ExxonMobil as a data scientist.
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We had layoffs.
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My own team had layoffs and everyone
on my team was like, oh my gosh,
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ExxonMobil, it's going in the pooper.
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It stinks.
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It's a bad company.
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Look at Meta Meta's basically
doubled their stock price recently.
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We need to get out of oil, we
need to get outta manufacturing.
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We need to get to big tech.
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'cause they're hiring so
many people right now.
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Their stock price is doing so well.
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Our stock price stinks.
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You know, we're on the decline.
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Everything's gonna go terribly.
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We should all leave and
we should go to meta.
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Now, watch what happened in 2021 and see
if we were right or if we were wrong.
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At the beginning of 2021, meta stock was
doing fine, but then it took a huge dive
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and basically lost 50% of their value.
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Meta had a ton of layoffs this year while
ExxonMobil doubled their stock price
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back to basically what it was originally.
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Now, Exxon was hiring data scientists
and Meta was laying off data scientists.
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The point here is if a company's
laying people off, you don't know
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if that's going to magically just
be the opposite the next year.
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And even if layoffs are happening
in the tech industry or whatever
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industry, there's probably another
industry that is booming that needs to
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hire data scientists, data engineers,
data analysts, data scientists.
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They're needed in every industry.
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Financing, consulting, manufacturing,
tech, like literally every
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industry needs data professionals.
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And just because the FANG companies
are laying people off doesn't mean
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that other companies, for instance,
like ExxonMobil aren't hiring.
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Let's flip over to 2023 to today, and
sure enough, meta, even though it seems
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like they might even do layoffs right
now, has four x their stock price and
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ExxonMobil is staying pretty steady.
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They're up about 20% still, and they're
still hiring at a very sustainable pace.
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My point here is don't stress because
people are doing layoffs or 'cause they're
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hiring or they're not hiring, because
you never know how it's affecting other
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industries and how that company might
actually just do hiring in the next year.
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By the way, I used AI almost exclusively
to create this chart right here.
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Pretty cool, right?
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Well, you're kind of wrong because
this chart sucked to make with ai.
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At first, I asked it to go download
the historic stock data for ExxonMobil
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and Meta, and it said that it did
it and it created these charts.
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But I looked at the data and some
things looked a little bit too
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perfect and a little bit too linear,
and so I went and investigated on my
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own, and sure enough, it literally
just made up the stock price data.
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It didn't get even remotely close.
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And I was about to show thousands
of you false data created by ai.
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Then it still took me like three hours to
make this chart, which honestly, I think
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I could have made this entire thing in
three hours just using Python with no ai.
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And finally I got to the chart where
it was almost ready to show you guys
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like I wanted to clean some things up.
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For instance, I wanted to move the title
from Behind these filters right here.
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I wanted to remove my grid lines and
I wanted to create some captions here.
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I ran out of Claude Credits
to actually edit this graph.
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So all of this to say, I
don't think you're cooked.
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I don't think data jobs are dead, and I
don't think AI is going to replace you.
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I think the data job market
right now is about what it
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should be in a tight economy.
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00:09:55,285 --> 00:09:59,365
So if you enjoyed this positive outlook,
do me a favor, hit like and hit subscribe
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because I have a lot more data content
I wanna share with you this year.
