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β TIMESTAMPS
ο»Ώ01:10 - Data-Driven Insights on the Job Market
02:18 - The Rise of Data Engineering
03:49 - AI's Impact on Data Roles
04:44 - Data Analyst Jobs Are Still Growing
06:27 - Job Hopping in Data Roles
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00:01:09
I'm going to be honest, the data job market has been really rough
00:01:13
the past year with the rise of AI layoffs, presidential political
00:01:18
turmoil, interest rates.
00:01:19
You're only really hearing a lot of negative things about the
00:01:23
data job market and tech job market in general.
00:01:25
You'll hear all these things on different social media platforms
00:01:28
like Threads or Twitter, or maybe some sort of mainstream media
00:01:32
platform like CNBC or Fox News or something like that.
00:01:36
But what's actually going on in the data job market right now?
00:01:39
Well, there's a lot of opinions.
00:01:40
You'll hear different things if you're on YouTube or if you're
00:01:42
listening via podcasts or on X or Threads or Facebook or from your
00:01:47
friends.
00:01:48
It's really hard.
00:01:49
And everyone kind of has a different opinion about it because
00:01:52
what's the actual truth?
00:01:54
No one really knows.
00:01:55
No one exactly really knows how the job market is going right
00:01:58
now.
00:01:58
And I can tell you what I'm experiencing from being a data analyst
00:02:02
career coach for over 600 different students.
00:02:04
I could tell you about posting every day and interacting on LinkedIn,
00:02:07
or from doing this podcast and talking to industry experts, you
00:02:10
know, people in the field.
00:02:11
But here's the truth.
00:02:12
Those would still just be kind of anecdotal opinions.
00:02:15
It's what I'm experiencing, it's what the people around me are
00:02:17
experiencing.
00:02:18
But it wouldn't be quite comprehensive, so.
00:02:20
But more importantly, it wouldn't really be data driven.
00:02:22
And it's always better to be data driven, especially on channels
00:02:26
like this.
00:02:26
We're data analysts, right?
00:02:27
We want to go off of what the data says.
00:02:30
Let's go ahead and dive into some data.
00:02:32
I was lucky to get my hands on this data.
00:02:34
This data was collected by a company I was recently introduced
00:02:37
to.
00:02:37
It's called Live Data Technologies and they track real
00:02:40
time employment data, leveraging publicly available data
00:02:43
sets.
00:02:44
So basically what the company does is monitor different platforms
00:02:47
and sees who's leaving jobs, who's coming into jobs.
00:02:50
They're basically looking around the Internet and publicly
00:02:52
available data sets and trying to make sense of it all.
00:02:54
The company sells the data and the insights that they pick up on
00:02:57
this data to product builders, investors, talent teams, all sorts
00:03:00
of different people.
00:03:01
And luckily for us, they've agreed to make some of this data
00:03:04
and some of these insights freely available to benefit the data
00:03:07
community.
00:03:07
So special shout out to them, specifically Jason Saltzman.
00:03:10
When I looked at this data, I had five main takeaways.
00:03:12
I had five things.
00:03:13
I was like, huh?
00:03:14
I didn't necessarily expect that.
00:03:15
Or I was like, oh, that's what I thought.
00:03:17
And this data confirms it.
00:03:18
And you want to make sure you stick around to the end because the
00:03:21
last one, I think that one will make you feel the best and the
00:03:24
most optimistic.
00:03:25
Spoiler alert.
00:03:26
All right, so let's dive into number one.
00:03:28
For a good portion of the 2010s, data scientist was labeled
00:03:32
the sexiest job of the 21st century.
00:03:34
And as a data scientist myself, I like to think that I'm
00:03:37
pretty sexy.
00:03:37
So I kind of agreed.
00:03:38
No, I'm just kidding.
00:03:39
The businesses really saw as a really sexy role and very valued
00:03:44
for their business.
00:03:45
You got paid a lot, you can work remotely, and that's still the
00:03:47
case.
00:03:48
But I would say that the data scientist role has kind of broken
00:03:50
up into different types of roles.
00:03:53
I think originally it was kind of just the data scientist role,
00:03:56
but like, now we see a lot more data engineers.
00:03:59
Now, data engineers did exist back then, but it wasn't nearly as
00:04:02
popular as it is now.
00:04:03
The other roles being created all the time, like analytics engineers,
00:04:05
one of the more new roles, um, so one of the things I looked into
00:04:09
is like, okay, with these different data job titles, which
00:04:12
one of these titles has had the most growth in the last five
00:04:15
years?
00:04:16
And it's not really a surprise.
00:04:18
It's data engineering.
00:04:19
There's a couple reasons behind this.
00:04:20
I think number one is we thought data science was sexy, and
00:04:24
it is sexy.
00:04:25
Doing things like machine learning, predicting things, using,
00:04:27
you know, AI, those types of things obviously is very cool.
00:04:30
But the problem is data science can't get a whole lot done
00:04:33
without a data engineer.
00:04:35
The data engineer needs to be there first to kind of set things
00:04:37
up, get the data all clean, prepped, storage usable in the right
00:04:40
ways.
00:04:41
And that just wasn't really the case in the early 2010s.
00:04:44
And so now we've seen this huge rise of data engineer, where
00:04:46
it's actually the fastest growing data role out there.
00:04:49
That's not to say that the data scientist isn't quick growing.
00:04:52
It's actually growing quite a bit as well.
00:04:54
It's just not growing as fast as it was maybe in early 2023, but
00:04:57
still growing quite a bit.
00:04:59
The other reason I think these data engineer jobs are being so in
00:05:02
demand in the last year and a half specifically, is due to AI.
00:05:05
AI is a really interesting problem because there's all these
00:05:07
AI models out there, but really the model is only as good
00:05:11
as the data you give it.
00:05:12
The better data you give it, the better the model is.
00:05:15
And also the more data you give it, the better the model is.
00:05:18
And data engineers have this unique skillset of being really equipped
00:05:22
to store data in correct places and make it easily accessible
00:05:25
to everywhere.
00:05:26
So data engineers are great fits for AI companies, AI products.
00:05:29
And so I think that's kind of why we're seeing a data engineer
00:05:31
boom right now, is because those skills are really in demand
00:05:34
now for the same reason with AI being good for data engineers,
00:05:38
is AI bad for data analysts?
00:05:40
And I can't even tell you how many messages I get of people asking
00:05:43
me, oh, like, is being a data analyst a good choice?
00:05:46
Is it going to be overtaken by AI?
00:05:48
Am I going to lose my job to AI in the next five years?
00:05:51
And let's go ahead and take this chart that we showed earlier.
00:05:53
Just focus on data analyst jobs in particular.
00:05:56
Take out the other job families and take a quick look.
00:05:58
So what you'll notice here is if we look at this graph and just
00:06:01
do the solo shot, is that data analyst jobs are still growing.
00:06:04
There's still growth over time.
00:06:06
Now you might be tempted to be like, no, Avery, look at the top
00:06:08
of that chart in the top right corner.
00:06:10
It's pretty stagnant.
00:06:11
Well, that's actually stagnant growth compared to 2019.
00:06:15
So the role is still growing at like 14% year over year when you
00:06:19
compare it to 2019.
00:06:21
So it's still growing quite a bit every single year.
00:06:24
Leads me to believe that data on this role is still a great role.
00:06:27
It's not being replaced by AI.
00:06:29
I don't really think it'll ever be replaced by AI.
00:06:31
But it's certainly not happening now and I don't really
00:06:34
see it happening down the road.
00:06:35
I see AI more as a tool that helps analysts analyze fafsa.
00:06:39
It's almost like when Microsoft Excel did, you know, the
00:06:42
data analysts then lose their job because all of a sudden we could
00:06:45
do these calculations in a computer.
00:06:47
No, it just helped them do their job faster.
00:06:49
So I see AI as a tool that helps analysts get their jobs done
00:06:52
quicker versus something that's going to ultimately replace
00:06:54
them.
00:06:54
It's a tool essentially like a hammer.
00:06:56
I think data analysts are still very valuable for companies.
00:06:59
They're providing them great insight at a little bit more of affordable
00:07:03
rate.
00:07:03
And it really helps these companies get like low hanging fruit
00:07:06
of all things in their data.
00:07:07
Because to be honest, AI is sexy, machine learning sexy, but
00:07:11
a lot of companies aren't there.
00:07:12
A lot of companies just need to be more data driven.
00:07:14
I think a data analyst is a great first step.
00:07:16
Trust me, there's so many Companies out there, like, like obviously
00:07:18
there's Google, there's Tesla, there's Facebook, where they're doing
00:07:21
cutting edge machine learning stuff all the time.
00:07:23
But for every one of those companies, honestly, there's probably
00:07:26
thousands of other companies who just need to make a report or
00:07:30
just had some data pulled in SQL like it's.
00:07:32
There's a lot of opportunities for data analysts out there.
00:07:35
And that was my second takeaway.
00:07:36
My third takeaway is that job hopping is in.
00:07:40
If you look at this chart right here, it'll show you the average
00:07:42
tenure of the different data job titles.
00:07:45
And that basically just shows you how long they're staying in a
00:07:48
specific role.
00:07:49
You might notice that database roles, they're staying there quite
00:07:51
a bit earlier.
00:07:52
The rest of these job families look like they're pretty similar
00:07:55
in terms of how long they're staying there.
00:07:57
And it ranges anywhere from two and a half to one and a half
00:07:59
years.
00:08:00
And what I get from this is that is the average that someone
00:08:02
is spending at a company before switching to a different company.
00:08:06
I think that's a good thing.
00:08:07
I think that should give you confidence to do it.
00:08:09
I think in the past it was frowned upon to leave a company early,
00:08:13
but now I think it's not nearly frowned upon as much.
00:08:15
I think more people are doing it and I think it's good because
00:08:18
I talked about this in my episode with Zach Wilson where he
00:08:21
discussed how he went from like $30 to like $500 in
00:08:25
like seven years or something like that.
00:08:26
And one of the reasons he was able to do it was he switched jobs
00:08:29
every 18 months.
00:08:30
And for some strange company, we live in an economy where you're
00:08:34
actually probably worth more to another company than your own.
00:08:37
They're willing to pay you more than your current company is,
00:08:40
which is weird and messed up and we could go into that.
00:08:42
But the point here is that it looks like everyone's job hopping.
00:08:45
And so you might consider as well point number four, and that
00:08:49
is that data hiring is happening literally in so many different
00:08:52
industries and so many different companies.
00:08:55
I'll pop up on the screen a couple graphs here.
00:08:56
We'll look at the first one, which is where companies are hiring
00:08:59
Data analysts in 2024.
00:09:01
And what you'll notice here is there's so many cool companies like
00:09:04
Capital One, Accenture, Deloitte, Data Annotation, Google.
00:09:07
What I want you to point out here is like, obviously Google's
00:09:10
here, obviously Tesla's on this list, Apple's on this list.
00:09:13
But there's a lot of like more traditional companies that aren't
00:09:16
like big tech companies that aren't fang companies.
00:09:18
And a lot of the times I think that we associate the data analyst
00:09:21
role with tech and because it is kind of a tech role.
00:09:24
But data analysts work at manufacturing companies, they work
00:09:26
at finance companies, they work at healthcare companies.
00:09:28
They don't only work at tech company companies.
00:09:31
The tech companies are kind of the sexy ones and they often have
00:09:34
a high salary.
00:09:35
But there's so many different roles at so many different companies
00:09:37
and sometimes I think we forget that that like it's not just
00:09:40
Facebook, it's not just Netflix that are hiring data people,
00:09:43
it's manufacturing companies, it's consulting companies like Deloitte,
00:09:46
it's healthcare companies like Optum.
00:09:48
There's more opportunities for data analytics outside of tech than
00:09:51
there is inside of tech.
00:09:52
And I think it's just a good reminder.
00:09:54
And then these graphs here that show what companies are hiring
00:09:56
the most, data engineers and data scientists.
00:09:58
I will point out that data scientist companies are a little
00:10:01
bit more of those tech companies met Microsoft, TikTok,
00:10:04
Google.
00:10:04
Right.
00:10:04
Those are a little bit more of what you typically feel in terms
00:10:07
of tech companies.
00:10:09
That being said, there's still consulting companies on this list,
00:10:11
there's still banks on this list, there's still finance companies
00:10:14
on this list, manufacturing companies.
00:10:16
So don't just think that it's only tech companies that are hiring
00:10:19
data roles.
00:10:20
Also, quick note.
00:10:21
It's interesting to see that Meta is leading and hiring both for
00:10:24
the data scientist and the data engineer position just because
00:10:27
they did pretty big layoffs like two years ago, year and a half
00:10:30
ago or something like that.
00:10:31
I think part of this was they just over hired during COVID for
00:10:35
different parts of their company and now they're kind of transitioning
00:10:39
into an AI company.
00:10:40
We'll see how that goes.
00:10:41
But I imagine they're hiring a lot of resources to do that.
00:10:44
And that's probably why you see such a big surge in data scientists
00:10:48
and data engineers.
00:10:49
But also Meta probably just hires quite a bit as well.
00:10:51
Okay, takeaway number five.
00:10:53
And this one is my favorite and that is that data jobs are quite
00:10:56
resilient.
00:10:57
This chart right here basically compares data scientist,
00:11:00
data engineer and data analyst levels to the average white collar
00:11:04
job levels.
00:11:05
Specifically what we're looking at is the percent of people
00:11:08
who are hired after leaving a role.
00:11:11
So basically the higher the percentage the better.
00:11:14
And what you can see that all three of the data job families are
00:11:17
higher than the average white collar worker, which basically means
00:11:20
that these jobs are in demand.
00:11:21
That means if someone in the data family is laid off, they're
00:11:24
more likely to glad a job quickly than your average white collar
00:11:28
worker.
00:11:28
Now that also could be true for if they're switching jobs as
00:11:31
well, which just allows more career flexibility.
00:11:33
Like we talked about earlier, job hopping usually means you're
00:11:36
making more money that way.
00:11:38
So.
00:11:38
So to me this is a great sign that basically data jobs are quite
00:11:41
resilient, they're quite flexible, and that no job is layoff
00:11:45
proof, of course.
00:11:45
But it does look like these data job families are still very
00:11:48
high in demand and will allow you to quickly land a job if you're
00:11:51
laid off or if you need to switch jobs for whatever reason.
00:11:53
With that, I hope you realize that the state of data jobs is maybe
00:11:57
not as bleak as you thought.
00:11:59
It may be things might seem grim, but honestly these numbers
00:12:03
look pretty healthy and I think we're in a good situation.
00:12:05
And I think that situation will continue into the next year
00:12:08
as well.
00:12:08
Thanks again to Live Data Technologies for sharing this data
00:12:11
with us.
00:12:12
I'll have a link to them down below in the show notes.
00:12:14
You guys can check them out.
00:12:15
And as always, if you're looking for another episode to watch,
00:12:18
I really suggest this one right here or in the show notes you
00:12:21
can find that linked as well.
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