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

I'm going to be honest, the data job market has been really rough the past year with the rise of AI layoffs, presidential political turmoil, interest rates.

You're only really hearing a lot of negative things about the data job market and tech job market in general.

You'll hear all these things on different social media platforms like Threads or Twitter, or maybe some sort of mainstream media platform like CNBC or Fox News or something like that.

But what's actually going on in the data job market right now?

Well, there's a lot of opinions.

You'll hear different things if you're on YouTube or if you're listening via podcasts or on X or Threads or Facebook or from your friends.

It's really hard.

And everyone kind of has a different opinion about it because what's the actual truth?

No one really knows.

No one exactly really knows how the job market is going right now.

And I can tell you what I'm experiencing from being a data analyst career coach for over 600 different students.

I could tell you about posting every day and interacting on LinkedIn, or from doing this podcast and talking to industry experts, you know, people in the field.

But here's the truth.

Those would still just be kind of anecdotal opinions.

It's what I'm experiencing, it's what the people around me are experiencing.

But it wouldn't be quite comprehensive, so.

But more importantly, it wouldn't really be data driven.

And it's always better to be data driven, especially on channels like this.

We're data analysts, right?

We want to go off of what the data says.

Let's go ahead and dive into some data.

I was lucky to get my hands on this data.

This data was collected by a company I was recently introduced to.

It's called Live Data Technologies and they track real time employment data, leveraging publicly available data sets.

So basically what the company does is monitor different platforms and sees who's leaving jobs, who's coming into jobs.

They're basically looking around the Internet and publicly available data sets and trying to make sense of it all.

The company sells the data and the insights that they pick up on this data to product builders, investors, talent teams, all sorts of different people.

And luckily for us, they've agreed to make some of this data and some of these insights freely available to benefit the data community.

So special shout out to them, specifically Jason Saltzman.

When I looked at this data, I had five main takeaways.

I had five things.

I was like, huh?

I didn't necessarily expect that.

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

And this data confirms it.

And you want to make sure you stick around to the end because the last one, I think that one will make you feel the best and the most optimistic.

Spoiler alert.

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

For a good portion of the 2010s, data scientist was labeled the sexiest job of the 21st century.

And as a data scientist myself, I like to think that I'm pretty sexy.

So I kind of agreed.

No, I'm just kidding.

The businesses really saw as a really sexy role and very valued for their business.

You got paid a lot, you can work remotely, and that's still the case.

But I would say that the data scientist role has kind of broken up into different types of roles.

I think originally it was kind of just the data scientist role, but like, now we see a lot more data engineers.

Now, data engineers did exist back then, but it wasn't nearly as popular as it is now.

The other roles being created all the time, like analytics engineers, one of the more new roles, um, so one of the things I looked into is like, okay, with these different data job titles, which one of these titles has had the most growth in the last five years?

And it's not really a surprise.

It's data engineering.

There's a couple reasons behind this.

I think number one is we thought data science was sexy, and it is sexy.

Doing things like machine learning, predicting things, using, you know, AI, those types of things obviously is very cool.

But the problem is data science can't get a whole lot done without a data engineer.

The data engineer needs to be there first to kind of set things up, get the data all clean, prepped, storage usable in the right ways.

And that just wasn't really the case in the early 2010s.

And so now we've seen this huge rise of data engineer, where it's actually the fastest growing data role out there.

That's not to say that the data scientist isn't quick growing.

It's actually growing quite a bit as well.

It's just not growing as fast as it was maybe in early 2023, but still growing quite a bit.

The other reason I think these data engineer jobs are being so in demand in the last year and a half specifically, is due to AI.

AI is a really interesting problem because there's all these AI models out there, but really the model is only as good as the data you give it.

The better data you give it, the better the model is.

And also the more data you give it, the better the model is.

And data engineers have this unique skillset of being really equipped to store data in correct places and make it easily accessible to everywhere.

So data engineers are great fits for AI companies, AI products.

And so I think that's kind of why we're seeing a data engineer boom right now, is because those skills are really in demand now for the same reason with AI being good for data engineers, is AI bad for data analysts?

And I can't even tell you how many messages I get of people asking me, oh, like, is being a data analyst a good choice?

Is it going to be overtaken by AI?

Am I going to lose my job to AI in the next five years?

And let's go ahead and take this chart that we showed earlier.

Just focus on data analyst jobs in particular.

Take out the other job families and take a quick look.

So what you'll notice here is if we look at this graph and just do the solo shot, is that data analyst jobs are still growing.

There's still growth over time.

Now you might be tempted to be like, no, Avery, look at the top of that chart in the top right corner.

It's pretty stagnant.

Well, that's actually stagnant growth compared to 2019.

So the role is still growing at like 14% year over year when you compare it to 2019.

So it's still growing quite a bit every single year.

Leads me to believe that data on this role is still a great role.

It's not being replaced by AI.

I don't really think it'll ever be replaced by AI.

But it's certainly not happening now and I don't really see it happening down the road.

I see AI more as a tool that helps analysts analyze fafsa.

It's almost like when Microsoft Excel did, you know, the data analysts then lose their job because all of a sudden we could do these calculations in a computer.

No, it just helped them do their job faster.

So I see AI as a tool that helps analysts get their jobs done quicker versus something that's going to ultimately replace them.

It's a tool essentially like a hammer.

I think data analysts are still very valuable for companies.

They're providing them great insight at a little bit more of affordable rate.

And it really helps these companies get like low hanging fruit of all things in their data.

Because to be honest, AI is sexy, machine learning sexy, but a lot of companies aren't there.

A lot of companies just need to be more data driven.

I think a data analyst is a great first step.

Trust me, there's so many Companies out there, like, like obviously there's Google, there's Tesla, there's Facebook, where they're doing cutting edge machine learning stuff all the time.

But for every one of those companies, honestly, there's probably thousands of other companies who just need to make a report or just had some data pulled in SQL like it's.

There's a lot of opportunities for data analysts out there.

And that was my second takeaway.

My third takeaway is that job hopping is in.

If you look at this chart right here, it'll show you the average tenure of the different data job titles.

And that basically just shows you how long they're staying in a specific role.

You might notice that database roles, they're staying there quite a bit earlier.

The rest of these job families look like they're pretty similar in terms of how long they're staying there.

And it ranges anywhere from two and a half to one and a half years.

And what I get from this is that is the average that someone is spending at a company before switching to a different company.

I think that's a good thing.

I think that should give you confidence to do it.

I think in the past it was frowned upon to leave a company early, but now I think it's not nearly frowned upon as much.

I think more people are doing it and I think it's good because I talked about this in my episode with Zach Wilson where he discussed how he went from like $30,000 to like $500,000 in like seven years or something like that.

And one of the reasons he was able to do it was he switched jobs every 18 months.

And for some strange company, we live in an economy where you're actually probably worth more to another company than your own.

They're willing to pay you more than your current company is, which is weird and messed up and we could go into that.

But the point here is that it looks like everyone's job hopping.

And so you might consider as well point number four, and that is that data hiring is happening literally in so many different industries and so many different companies.

I'll pop up on the screen a couple graphs here.

We'll look at the first one, which is where companies are hiring Data analysts in 2024.

And what you'll notice here is there's so many cool companies like Capital One, Accenture, Deloitte, Data Annotation, Google.

What I want you to point out here is like, obviously Google's here, obviously Tesla's on this list, Apple's on this list.

But there's a lot of like more traditional companies that aren't like big tech companies that aren't fang companies.

And a lot of the times I think that we associate the data analyst role with tech and because it is kind of a tech role.

But data analysts work at manufacturing companies, they work at finance companies, they work at healthcare companies.

They don't only work at tech company companies.

The tech companies are kind of the sexy ones and they often have a high salary.

But there's so many different roles at so many different companies and sometimes I think we forget that that like it's not just Facebook, it's not just Netflix that are hiring data people, it's manufacturing companies, it's consulting companies like Deloitte, it's healthcare companies like Optum.

There's more opportunities for data analytics outside of tech than there is inside of tech.

And I think it's just a good reminder.

And then these graphs here that show what companies are hiring the most, data engineers and data scientists.

I will point out that data scientist companies are a little bit more of those tech companies met Microsoft, TikTok, Google.

Right.

Those are a little bit more of what you typically feel in terms of tech companies.

That being said, there's still consulting companies on this list, there's still banks on this list, there's still finance companies on this list, manufacturing companies.

So don't just think that it's only tech companies that are hiring data roles.

Also, quick note.

It's interesting to see that Meta is leading and hiring both for the data scientist and the data engineer position just because they did pretty big layoffs like two years ago, year and a half ago or something like that.

I think part of this was they just over hired during COVID for different parts of their company and now they're kind of transitioning into an AI company.

We'll see how that goes.

But I imagine they're hiring a lot of resources to do that.

And that's probably why you see such a big surge in data scientists and data engineers.

But also Meta probably just hires quite a bit as well.

Okay, takeaway number five.

And this one is my favorite and that is that data jobs are quite resilient.

This chart right here basically compares data scientist, data engineer and data analyst levels to the average white collar job levels.

Specifically what we're looking at is the percent of people who are hired after leaving a role.

So basically the higher the percentage the better.

And what you can see that all three of the data job families are higher than the average white collar worker, which basically means that these jobs are in demand.

That means if someone in the data family is laid off, they're more likely to glad a job quickly than your average white collar worker.

Now that also could be true for if they're switching jobs as well, which just allows more career flexibility.

Like we talked about earlier, job hopping usually means you're making more money that way.

So.

So to me this is a great sign that basically data jobs are quite resilient, they're quite flexible, and that no job is layoff proof, of course.

But it does look like these data job families are still very high in demand and will allow you to quickly land a job if you're laid off or if you need to switch jobs for whatever reason.

With that, I hope you realize that the state of data jobs is maybe not as bleak as you thought.

It may be things might seem grim, but honestly these numbers look pretty healthy and I think we're in a good situation.

And I think that situation will continue into the next year as well.

Thanks again to Live Data Technologies for sharing this data with us.

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

You guys can check them out.

And as always, if you're looking for another episode to watch, I really suggest this one right here or in the show notes you can find that linked as well.