133: My Honest Thoughts on The Data Job Market in 2024

Avery: 00:00:00

I'm going to be honest, the data job market has been

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really rough the past year.

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With the rise of AI, layoffs, presidential

political turmoil, interest rates,

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you're only really hearing a lot of

negative things about the data job

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market and tech job market in general.

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You'll hear all these things on

different social media platforms

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like threads or twitter or maybe some

sort of mainstream media platform.

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Platform like CNBC or Fox

News or something like that.

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But what's actually going on in

the data job market right now?

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Well, there's a lot of opinions.

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You'll hear different things if you're

on YouTube or if you're listening via

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podcasts or on X or threads or Facebook

or from your friends, it's really hard.

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And everyone kind of has a

different opinion about it because.

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What's the actual truth?

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No one really knows.

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No one exactly really knows how

the job market is going right now.

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And I can tell you what I'm experiencing

from being a data analyst, career

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coach for over 60 different students.

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I can tell you about posting every day

and interacting on LinkedIn or from doing

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this podcast and talking to industry

experts, you know, people in the field.

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But here's the truth.

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Those would still just be

kind of anecdotal opinions.

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It's what I'm experiencing.

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It's what the people around

me are experiencing, but it

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wouldn't be quite comprehensive.

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So, but more importantly, it

wouldn't really be data driven.

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And it's always better to be data

driven, especially on channels like this.

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We're data analysts, right?

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We want to go off of what the data says.

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Let's go ahead and dive into some data.

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I was lucky to get my hands on this data.

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This data was collected by a company

I was recently introduced to.

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It's called Live Data Technologies,

and they track real time employees

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Employment data, leveraging

publicly available data sets.

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So basically what the company does is

monitor different platforms and sees who's

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leaving jobs, who's coming into jobs.

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They're basically looking around the

internet and publicly available data

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sets and trying to make sense of it all.

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The company sells the data and

the insights that they produce.

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Pick up on this data to product

builders, investors, talent teams,

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all sorts of different people.

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And luckily for us, they've agreed

to make some of this data and some

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of these insights freely available

to benefit the data community.

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So special shout out to them

specifically Jason Saltzman.

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When I looked at this data,

I had five main takeaways.

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I had five things that I was like,

huh, I didn't necessarily expect that.

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Or I was like, oh, that's what I thought.

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And this data confirms it.

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And you want to make sure you stick

around to the end because the last one.

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I think that one will make

you feel the best and the

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most optimistic spoiler alert.

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All right, so let's dive into number one.

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For a good portion of the 2010s,

data scientists was labeled the

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sexiest job of the 21st century.

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And as a data scientist myself, I

like to think that I'm pretty sexy.

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So I kind of agree.

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No, I'm just kidding.

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The businesses really saw it

as a really sexy role and very

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valued for their business.

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And you got paid a lot.

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You can work remotely and

that's still the case.

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But I would say that the

data scientists role.

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Uh, it's kind of broken up

into different types of roles.

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I think originally it was kind of

just the data scientist role, but like

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now we see a lot more data engineers.

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Now data engineers did exist

back then, but it wasn't

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nearly as popular as it is now.

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There's other roles being created

all the time, like analytics engineer

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is one of the more new roles.

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Um, so one of the things I

looked into is like, okay, with

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these different data job titles.

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Um, yeah.

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Yeah.

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Which one of these titles have had the

most growth in the last five years?

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And it's not really a surprise.

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It's data engineering.

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There's a couple reasons

behind this, I think.

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Number one is we thought data

science was sexy, and it is sexy.

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Doing things like machine learning,

predicting things, using, you

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know, AI, those types of things.

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Obviously is very cool, but the problem

is data science can't get a whole lot

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done without a data engineer The data

engineer needs to be there first to kind

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of set things up get the data all clean

prepped stored Usable in the right ways

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and that just wasn't really the case in

the early: 2010

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this huge rise of data engineer where

it's actually the fastest growing data

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role out there That's not to say that the

data scientist It's not quick growing, but

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it's actually growing quite a bit as well.

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It's just not growing as fast

as it was maybe in early: 2023

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But still growing quite a bit.

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The other reason I think these

data engineer jobs are being so

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in demand in the last year and a

half specifically is due to AI.

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AI is a really interesting problem

because There's all these AI models

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out there, but really the model is

only as good as the data you give it.

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The better data you give it, the better

the model is, and also the more data

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you give it, the better the model is.

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And data engineers have this unique

skill set of being really equipped to

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store data incorrect places and make

it easily accessible to everyone.

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So data engineers are great fits

for AI companies and AI products.

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And so I think that's kind of why we're

seeing a data engineer boom right now is

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because those skills are really in demand.

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Now for the same reason with with AI

being good for data engineers, is AI bad?

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For data analysts, and I can't even

tell you how many messages I get

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of people asking me, oh, like, is

being a data analyst a good choice?

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Is it gonna be overtaken by ai?

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Am I going to lose my job to

AI in the next five years?

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And let's go ahead and take this

chart that we showed earlier.

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Just focus on data analyst jobs in

particular, take out the other job

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families and take a quick look.

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So what you'll notice here is if we look

at this graph and just do the solo shot.

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Is that data analyst

jobs are still growing.

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There's still growth over time.

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Now you might be tempted to be

like, no, Avery, look at the top

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of that chart in the top right

corner, it's pretty stagnant.

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Well, that's actually stagnant

growth compared to: 2019

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So the role is still growing at

like 14 percent year over year

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when you compare it to 2019.

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So it's still growing quite

a bit every single year.

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Leads me to believe that data

analyst role is still a great role.

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It's not being replaced by AI.

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I don't really think it'll ever

be replaced by AI, but it's

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certainly not happening now.

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And I don't really see it

happening down the road.

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I see AI more as a tool that

helps analysts analyze faster.

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It's almost like when Microsoft Excel

did, you know, the data analysts then

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lose their job because all of a sudden we

could do these calculations in a computer.

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No, it just helped them

do their job faster.

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So I see AI as a tool that helps

analysts get their jobs done

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quicker versus something that's

going to ultimately replace them.

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It's a tool essentially, like a hammer.

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I think data analysts are still

very valuable for companies.

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They're providing them great insight at

a little bit more of affordable rate.

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And it really helps these companies

get like the low hanging fruit

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of all things in their data.

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Because to be honest, AI is sexy,

machine learning is sexy, but a

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lot of companies aren't there.

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A lot of companies just

need to be more data driven.

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And I think a data analyst

is a great Trust me, there's

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so many companies out there.

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Like, like, obviously there's Google,

there's Tesla, there's Facebook

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where they're doing cutting edge

machine learning stuff all the time.

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But for every one of those

companies, honestly, there's probably

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thousands of other companies who

just need to make a report or

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just had some data pulled in SQL.

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Like it's, there's a lot of opportunities

for data analysts out there.

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And that was my second takeaway.

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My third is that job hopping is, if

you look at this chart right here,

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it'll show you the average tenure

of the different data job titles.

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And that basically just shows you how

long they're staying in a specific role.

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You might notice that database roles,

they're staying there quite a bit earlier.

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The rest of these job families look

like they're pretty similar in terms

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of how long they're staying there.

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And it ranges anywhere

from two and a half.

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to one and a half years.

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And what I get from this is that

is the average that someone is

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spending at a company before

switching to a different company.

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I think that's a good thing.

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I think that should give

you confidence to do it.

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I think in the past it was frowned upon

to leave a company early, but now I think

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it's not nearly frowned upon as much.

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I think more people are doing it and I

think it's good because I talked about

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this in my episode with Zach Wilson,

where he discussed how he went from

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like 30, 000 to like 500, 000 in like

seven years or something like that.

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And one of the reasons he was able to do

it was he switched jobs every 18 months.

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And for some strange company, we live

in an economy where you're actually

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probably worth more to another company

than your own, they're willing to pay

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you more than your current company

is, which is weird and messed up.

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And we can go into that, but.

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The point here is that it looks

like everyone's job hopping.

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And so you might consider it as well.

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Point number four.

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And that is that data hiring is happening

literally in so many different industries

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and so many different companies.

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Uh, I'll pop up on the screen,

a couple of graphs here.

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We'll look at the first one,

which is where companies are

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hiring data analysts in 2024.

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And what you'll notice here is

there's so many cool companies

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like Capital One, Accenture,

Deloitte, Data Annotation, Google.

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What I want you to point out here

is like, Obviously, Google's here.

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Obviously, Tesla's on this list.

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Apple's on this list.

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But there's a lot of like more traditional

companies that aren't like big tech

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companies that aren't fang companies.

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And a lot of the times I think that we

associate the data analyst role with tech

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and because it is kind of a tech role,

but data analysts work at manufacturing

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companies, they work at finance companies,

they work at healthcare companies.

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They don't only work at tech companies.

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The tech companies are kind of the

sexy ones, and they often have a high

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salary, but there's so many different

roles at so many different companies.

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And sometimes I think we forget that,

that like, it's not just Facebook.

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It's not just Netflix that

are hiring data people.

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It's manufacturing companies.

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It's consulting companies like Deloitte.

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It's healthcare companies like Optum.

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There's more opportunities for

data analytics outside of tech

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than there is inside of tech.

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And I think And then these graphs here

that show what companies are hiring the

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most data engineers and data scientists.

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I will point out that data

scientist companies are a little

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bit more of those tech companies.

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Meta, Microsoft, TikTok, Google, right?

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Those are a little bit more of what you

typically feel in terms of tech companies.

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That being said, there's still

consulting companies on this list.

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There's still banks on this list.

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There's still finance companies on

this list, manufacturing companies.

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So don't just think that it's only tech

companies that are hiring data scientists.

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Data roles.

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Also quick note, it's interesting to see

that Meta is leading and hiring both for

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the data scientist and the data engineer

position just because they did pretty

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big layoffs like two years ago, year

and a half ago or something like that.

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I think part of this was they just

overhired during COVID for different

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parts of their company and now they're

kind of transitioning into an AI company.

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We'll see how that goes, but I imagine

they're hiring a lot of resources

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to do that and that's probably why

you see such a big surge in data

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scientists and data engineers.

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Um, but also Meta probably

just hires quite a bit as well.

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Okay, takeaway number five, and

this one is my favorite, and that is

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that data jobs are quite resilient.

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This chart right here basically

compares data scientist, data

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engineer, and data analyst levels to

the average white collar job levels.

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Specifically, what we're looking

at is the percent of people who

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are hired after leaving a role.

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So basically, the higher

the percentage, the better.

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Um, and what you can see that all

three of the data job families

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are higher than the average white

collar worker, which basically

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means that these jobs are in demand.

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That means if someone in the data

family is laid off, they are more

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likely to land a job quickly than

your average white collar worker.

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Now that also could be true for if

they're switching jobs as well, which

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just allows more career flexibility.

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And like we talked about earlier,

job hopping usually means you're

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making more money that way.

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So to me, this is a great sign that

basically data jobs are quite resilient.

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I think they're quite.

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flexible and uh, no job is layoff proof

of course, but it does look like these

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data job families are still very high

in demand and will allow you to quickly

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land a job if you're laid off or if you

need to switch jobs for whatever reason.

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With that, I hope you realize that

the state of data jobs is maybe not

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as bleak as you thought it may be.

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Things might seem grim but honestly

these numbers look pretty healthy

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and I think we're in a good situation

and I think that situation will

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continue into the next year as well.

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Thanks again to Live Data Technologies

for sharing this data with us.

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I'll have a link to them down below in the

show notes you guys can check them out.

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And as always if you're looking for

another episode to watch I really suggest

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this one right here or in the show

notes you can find that link as well.

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