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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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00:00:00
Avery: I'm going to be honest,
the data job market has been
00:00:02
really rough the past year.
00:00:04
With the rise of AI, layoffs, presidential
political turmoil, interest rates,
00:00:10
you're only really hearing a lot of
negative things about the data job
00:00:14
market and tech job market in general.
00:00:16
You'll hear all these things on
different social media platforms
00:00:19
like threads or twitter or maybe some
sort of mainstream media platform.
00:00:22
Platform like CNBC or Fox
News or something like that.
00:00:26
But what's actually going on in
the data job market right now?
00:00:29
Well, there's a lot of opinions.
00:00:30
You'll hear different things if you're
on YouTube or if you're listening via
00:00:33
podcasts or on X or threads or Facebook
or from your friends, it's really hard.
00:00:39
And everyone kind of has a
different opinion about it because.
00:00:43
What's the actual truth?
00:00:44
No one really knows.
00:00:45
No one exactly really knows how
the job market is going right now.
00:00:49
And I can tell you what I'm experiencing
from being a data analyst, career
00:00:53
coach for over 60 different students.
00:00:54
I can tell you about posting every day
and interacting on LinkedIn or from doing
00:00:58
this podcast and talking to industry
experts, you know, people in the field.
00:01:01
But here's the truth.
00:01:02
Those would still just be
kind of anecdotal opinions.
00:01:05
It's what I'm experiencing.
00:01:07
It's what the people around
me are experiencing, but it
00:01:08
wouldn't be quite comprehensive.
00:01:10
So, but more importantly, it
wouldn't really be data driven.
00:01:13
And it's always better to be data
driven, especially on channels like this.
00:01:17
We're data analysts, right?
00:01:18
We want to go off of what the data says.
00:01:20
Let's go ahead and dive into some data.
00:01:23
I was lucky to get my hands on this data.
00:01:24
This data was collected by a company
I was recently introduced to.
00:01:28
It's called Live Data Technologies,
and they track real time employees
00:01:31
Employment data, leveraging
publicly available data sets.
00:01:34
So basically what the company does is
monitor different platforms and sees who's
00:01:39
leaving jobs, who's coming into jobs.
00:01:40
They're basically looking around the
internet and publicly available data
00:01:43
sets and trying to make sense of it all.
00:01:45
The company sells the data and
the insights that they produce.
00:01:47
Pick up on this data to product
builders, investors, talent teams,
00:01:51
all sorts of different people.
00:01:52
And luckily for us, they've agreed
to make some of this data and some
00:01:54
of these insights freely available
to benefit the data community.
00:01:57
So special shout out to them
specifically Jason Saltzman.
00:02:00
When I looked at this data,
I had five main takeaways.
00:02:02
I had five things that I was like,
huh, I didn't necessarily expect that.
00:02:06
Or I was like, oh, that's what I thought.
00:02:08
And this data confirms it.
00:02:09
And you want to make sure you stick
around to the end because the last one.
00:02:12
I think that one will make
you feel the best and the
00:02:15
most optimistic spoiler alert.
00:02:17
All right, so let's dive into number one.
00:02:18
For a good portion of the 2010s,
data scientists was labeled the
00:02:22
sexiest job of the 21st century.
00:02:24
And as a data scientist myself, I
like to think that I'm pretty sexy.
00:02:28
So I kind of agree.
00:02:29
No, I'm just kidding.
00:02:29
The businesses really saw it
as a really sexy role and very
00:02:34
valued for their business.
00:02:35
And you got paid a lot.
00:02:36
You can work remotely and
that's still the case.
00:02:38
But I would say that the
data scientists role.
00:02:40
Uh, it's kind of broken up
into different types of roles.
00:02:43
I think originally it was kind of
just the data scientist role, but like
00:02:46
now we see a lot more data engineers.
00:02:49
Now data engineers did exist
back then, but it wasn't
00:02:52
nearly as popular as it is now.
00:02:54
There's other roles being created
all the time, like analytics engineer
00:02:56
is one of the more new roles.
00:02:58
Um, so one of the things I
looked into is like, okay, with
00:03:00
these different data job titles.
00:03:02
Um, yeah.
00:03:02
Yeah.
00:03:02
Which one of these titles have had the
most growth in the last five years?
00:03:06
And it's not really a surprise.
00:03:08
It's data engineering.
00:03:09
There's a couple reasons
behind this, I think.
00:03:11
Number one is we thought data
science was sexy, and it is sexy.
00:03:15
Doing things like machine learning,
predicting things, using, you
00:03:18
know, AI, those types of things.
00:03:20
Obviously is very cool, but the problem
is data science can't get a whole lot
00:03:23
done without a data engineer The data
engineer needs to be there first to kind
00:03:27
of set things up get the data all clean
prepped stored Usable in the right ways
00:03:31
and that just wasn't really the case in
the early 2010s And so now we've seen
00:03:35
this huge rise of data engineer where
it's actually the fastest growing data
00:03:39
role out there That's not to say that the
data scientist It's not quick growing, but
00:03:43
it's actually growing quite a bit as well.
00:03:45
It's just not growing as fast
as it was maybe in early 2023.
00:03:48
But still growing quite a bit.
00:03:49
The other reason I think these
data engineer jobs are being so
00:03:52
in demand in the last year and a
half specifically is due to AI.
00:03:55
AI is a really interesting problem
because There's all these AI models
00:03:59
out there, but really the model is
only as good as the data you give it.
00:04:03
The better data you give it, the better
the model is, and also the more data
00:04:06
you give it, the better the model is.
00:04:08
And data engineers have this unique
skill set of being really equipped to
00:04:13
store data incorrect places and make
it easily accessible to everyone.
00:04:16
So data engineers are great fits
for AI companies and AI products.
00:04:19
And so I think that's kind of why we're
seeing a data engineer boom right now is
00:04:22
because those skills are really in demand.
00:04:24
Now for the same reason with with AI
being good for data engineers, is AI bad?
00:04:29
For data analysts, and I can't even
tell you how many messages I get
00:04:33
of people asking me, oh, like, is
being a data analyst a good choice?
00:04:36
Is it gonna be overtaken by ai?
00:04:38
Am I going to lose my job to
AI in the next five years?
00:04:41
And let's go ahead and take this
chart that we showed earlier.
00:04:44
Just focus on data analyst jobs in
particular, take out the other job
00:04:47
families and take a quick look.
00:04:49
So what you'll notice here is if we look
at this graph and just do the solo shot.
00:04:52
Is that data analyst
jobs are still growing.
00:04:55
There's still growth over time.
00:04:56
Now you might be tempted to be
like, no, Avery, look at the top
00:04:58
of that chart in the top right
corner, it's pretty stagnant.
00:05:02
Well, that's actually stagnant
growth compared to 2019.
00:05:06
So the role is still growing at
like 14 percent year over year
00:05:09
when you compare it to 2019.
00:05:11
So it's still growing quite
a bit every single year.
00:05:15
Leads me to believe that data
analyst role is still a great role.
00:05:18
It's not being replaced by AI.
00:05:19
I don't really think it'll ever
be replaced by AI, but it's
00:05:22
certainly not happening now.
00:05:23
And I don't really see it
happening down the road.
00:05:25
I see AI more as a tool that
helps analysts analyze faster.
00:05:29
It's almost like when Microsoft Excel
did, you know, the data analysts then
00:05:34
lose their job because all of a sudden we
could do these calculations in a computer.
00:05:37
No, it just helped them
do their job faster.
00:05:39
So I see AI as a tool that helps
analysts get their jobs done
00:05:42
quicker versus something that's
going to ultimately replace them.
00:05:45
It's a tool essentially, like a hammer.
00:05:47
I think data analysts are still
very valuable for companies.
00:05:50
They're providing them great insight at
a little bit more of affordable rate.
00:05:53
And it really helps these companies
get like the low hanging fruit
00:05:56
of all things in their data.
00:05:58
Because to be honest, AI is sexy,
machine learning is sexy, but a
00:06:01
lot of companies aren't there.
00:06:02
A lot of companies just
need to be more data driven.
00:06:05
And I think a data analyst
is a great Trust me, there's
00:06:07
so many companies out there.
00:06:08
Like, like, obviously there's Google,
there's Tesla, there's Facebook
00:06:11
where they're doing cutting edge
machine learning stuff all the time.
00:06:13
But for every one of those
companies, honestly, there's probably
00:06:16
thousands of other companies who
just need to make a report or
00:06:20
just had some data pulled in SQL.
00:06:22
Like it's, there's a lot of opportunities
for data analysts out there.
00:06:25
And that was my second takeaway.
00:06:27
My third is that job hopping is, if
you look at this chart right here,
00:06:32
it'll show you the average tenure
of the different data job titles.
00:06:35
And that basically just shows you how
long they're staying in a specific role.
00:06:39
You might notice that database roles,
they're staying there quite a bit earlier.
00:06:43
The rest of these job families look
like they're pretty similar in terms
00:06:46
of how long they're staying there.
00:06:47
And it ranges anywhere
from two and a half.
00:06:49
to one and a half years.
00:06:50
And what I get from this is that
is the average that someone is
00:06:53
spending at a company before
switching to a different company.
00:06:56
I think that's a good thing.
00:06:58
I think that should give
you confidence to do it.
00:07:00
I think in the past it was frowned upon
to leave a company early, but now I think
00:07:04
it's not nearly frowned upon as much.
00:07:06
I think more people are doing it and I
think it's good because I talked about
00:07:09
this in my episode with Zach Wilson,
where he discussed how he went from
00:07:13
like 30, 000 to like 500, 000 in like
seven years or something like that.
00:07:17
And one of the reasons he was able to do
it was he switched jobs every 18 months.
00:07:21
And for some strange company, we live
in an economy where you're actually
00:07:24
probably worth more to another company
than your own, they're willing to pay
00:07:28
you more than your current company
is, which is weird and messed up.
00:07:32
And we can go into that, but.
00:07:33
The point here is that it looks
like everyone's job hopping.
00:07:36
And so you might consider it as well.
00:07:38
Point number four.
00:07:39
And that is that data hiring is happening
literally in so many different industries
00:07:43
and so many different companies.
00:07:45
Uh, I'll pop up on the screen,
a couple of graphs here.
00:07:47
We'll look at the first one,
which is where companies are
00:07:49
hiring data analysts in 2024.
00:07:52
And what you'll notice here is
there's so many cool companies
00:07:54
like Capital One, Accenture,
Deloitte, Data Annotation, Google.
00:07:57
What I want you to point out here
is like, Obviously, Google's here.
00:08:00
Obviously, Tesla's on this list.
00:08:02
Apple's on this list.
00:08:03
But there's a lot of like more traditional
companies that aren't like big tech
00:08:07
companies that aren't fang companies.
00:08:08
And a lot of the times I think that we
associate the data analyst role with tech
00:08:12
and because it is kind of a tech role,
but data analysts work at manufacturing
00:08:16
companies, they work at finance companies,
they work at healthcare companies.
00:08:19
They don't only work at tech companies.
00:08:21
The tech companies are kind of the
sexy ones, and they often have a high
00:08:25
salary, but there's so many different
roles at so many different companies.
00:08:27
And sometimes I think we forget that,
that like, it's not just Facebook.
00:08:31
It's not just Netflix that
are hiring data people.
00:08:33
It's manufacturing companies.
00:08:34
It's consulting companies like Deloitte.
00:08:36
It's healthcare companies like Optum.
00:08:38
There's more opportunities for
data analytics outside of tech
00:08:41
than there is inside of tech.
00:08:42
And I think And then these graphs here
that show what companies are hiring the
00:08:47
most data engineers and data scientists.
00:08:49
I will point out that data
scientist companies are a little
00:08:51
bit more of those tech companies.
00:08:52
Meta, Microsoft, TikTok, Google, right?
00:08:55
Those are a little bit more of what you
typically feel in terms of tech companies.
00:08:59
That being said, there's still
consulting companies on this list.
00:09:02
There's still banks on this list.
00:09:03
There's still finance companies on
this list, manufacturing companies.
00:09:06
So don't just think that it's only tech
companies that are hiring data scientists.
00:09:10
Data roles.
00:09:10
Also quick note, it's interesting to see
that Meta is leading and hiring both for
00:09:14
the data scientist and the data engineer
position just because they did pretty
00:09:18
big layoffs like two years ago, year
and a half ago or something like that.
00:09:22
I think part of this was they just
overhired during COVID for different
00:09:26
parts of their company and now they're
kind of transitioning into an AI company.
00:09:30
We'll see how that goes, but I imagine
they're hiring a lot of resources
00:09:34
to do that and that's probably why
you see such a big surge in data
00:09:38
scientists and data engineers.
00:09:39
Um, but also Meta probably
just hires quite a bit as well.
00:09:42
Okay, takeaway number five, and
this one is my favorite, and that is
00:09:45
that data jobs are quite resilient.
00:09:48
This chart right here basically
compares data scientist, data
00:09:50
engineer, and data analyst levels to
the average white collar job levels.
00:09:55
Specifically, what we're looking
at is the percent of people who
00:09:58
are hired after leaving a role.
00:10:01
So basically, the higher
the percentage, the better.
00:10:04
Um, and what you can see that all
three of the data job families
00:10:07
are higher than the average white
collar worker, which basically
00:10:10
means that these jobs are in demand.
00:10:12
That means if someone in the data
family is laid off, they are more
00:10:15
likely to land a job quickly than
your average white collar worker.
00:10:18
Now that also could be true for if
they're switching jobs as well, which
00:10:22
just allows more career flexibility.
00:10:24
And like we talked about earlier,
job hopping usually means you're
00:10:27
making more money that way.
00:10:28
So to me, this is a great sign that
basically data jobs are quite resilient.
00:10:32
I think they're quite.
00:10:32
flexible and uh, no job is layoff proof
of course, but it does look like these
00:10:37
data job families are still very high
in demand and will allow you to quickly
00:10:40
land a job if you're laid off or if you
need to switch jobs for whatever reason.
00:10:44
With that, I hope you realize that
the state of data jobs is maybe not
00:10:48
as bleak as you thought it may be.
00:10:50
Things might seem grim but honestly
these numbers look pretty healthy
00:10:54
and I think we're in a good situation
and I think that situation will
00:10:57
continue into the next year as well.
00:10:59
Thanks again to Live Data Technologies
for sharing this data with us.
00:11:02
I'll have a link to them down below in the
show notes you guys can check them out.
00:11:06
And as always if you're looking for
another episode to watch I really suggest
00:11:09
this one right here or in the show
notes you can find that link as well.