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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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https://datacareerjumpstart.com/daa

Avery Smith-1: If you're breaking

into data analytics right now, you're

2

probably pretty depressed and pretty

anxious with everything that's going on.

3

It feels like there's no data jobs left.

4

The few data jobs that are left are

uber competitive, and the rest of

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the data world is just going to be

replaced by AI by the end of the year.

6

If you feel this way, I don't blame you.

7

It's super easy to fail this way in

today's market, but I'm going to share

8

some raw, transparent numbers that

I think is gonna give you a little

9

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

45

and analytics in the past decade.

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And now data engineering is

just kind of catching up.

47

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.

88

By the way, this is the type of analysis

graphs and data that I try to share

89

every week in my newsletter that's

specifically for data professionals.

90

It's a hundred percent

free and it's 25,000.

91

Other aspiring data professionals have

already joined, so why not join them?

92

Go to data career jumpstart.com/newsletter

93

or click the link, the

description down below.

94

The next question you might be asking

is, well, there's no data jobs left.

95

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.

104

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,

115

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.

126

They're a data product, and if you listen

to an episode that I released recently,

127

or you're subscribed to my newsletter,

you learned what a data product is.

128

But basically they sell data as a service

and they're tracking working professionals

129

in real time so that you can actually

see where people are going, how companies

130

are shifting, what roles are going up,

what roles are going down, what companies

131

are actually hiring, what companies

are firing, those types of things.

132

And they were actually kind enough

to send this data to me and let

133

me share it with all of you.

134

So if you wanna learn more about

them, you can check their link

135

in the show notes down below.

136

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.

138

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.

144

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.

148

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

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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.

163

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.

167

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.

171

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.

173

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

183

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

187

like they might even do layoffs right

now, has four x their stock price and

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ExxonMobil is staying pretty steady.

189

They're up about 20% still, and they're

still hiring at a very sustainable pace.

190

My point here is don't stress because

people are doing layoffs or 'cause they're

191

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.

196

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.

198

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.

207

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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So if you enjoyed this positive outlook,

do me a favor, hit like and hit subscribe

215

because I have a lot more data content

I wanna share with you this year.