208: I Analyzed 8,553 Data Analyst Salaries — Here's What They're ACTUALLY Paying in 2026 - Data Career Podcast: Helping You Land a Data Analyst Job FAST

Speaker: 00:00:00

So I just analyzed 8,554 data analyst

jobs to find out exactly what they

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are paying right now and the results.

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They even shocked me.

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And I look at data, job

listings for a living.

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So in this episode, I'll break

down 8,000 different salaries.

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In every way that you possibly can by

experience level, by job title, by remote

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versus in office, by state and by skill.

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And I'll show you the highest paid

job and the lowest paid job and

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exactly what you need to do to land

that $212,000 data analyst role.

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So let's go ahead and get into it.

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By the way, if you're new

here, my name is Avery Smith.

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I'm a senior data analyst with 10 years

of experience, and now I spend all of my

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time trying to help people like you land.

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Data jobs and everything that I'm going

to be showing you all the data and all

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the graphs and all the salaries is going

to be coming from Find a data job.com,

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which is actually a data analyst

job board where that you can

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use to find data analyst jobs.

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It's actually one that I run and we

post dozens of data jobs for free.

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Online every day that you

guys can apply for right now.

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So if you haven't bookmarked it

yet, please go ahead and do so.

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It's a really useful resource

and there'll be a link in the

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description down below as well.

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And all the graphs and data I'll

be showing you is available as well

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in our salary report right here.

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And we'll have a link to it

in the show notes down below.

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And if you're listening only via audio

on Spotify or Apple Podcast or something,

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I'm gonna do my absolute best to narrate

everything that I'm showing today.

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That way you can basically

picture the graphs in your head.

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Okay, so let's get to what actually

really shocked me right away, and there's

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a few things I really wanna highlight.

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The first one is actually

this median salary.

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The median salary of the 8,553

jobs I looked at was basically 92.

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Thousand dollars.

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And I thought that was pretty impressive

because if you go online and you

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check like Indeed or Glassdoor,

they're gonna tell you the median

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salary is like $82,000 or $85,000.

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And I'm saying it's about seven to

$10,000 more, which is like what?

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10 plus percent more.

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It's not insignificant.

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Now, of course our data

sets are very different.

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We have different amount of jobs,

different types of jobs, so on

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and so forth, so, so it's not

something to get too bogged down

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in, but I think this is a good sign.

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At least the jobs that I'm

posting on find data job.com

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have a little bit higher

salary on general and average.

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So another reason you guys should

be using find data job.com.

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The other thing that blew me away is the

stats right here that out of the 8,553

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jobs that I analyzed, only 3,451 of

them had anything to do with salaries.

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Had only any mention of

salary or salary data.

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That's only 40%.

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Meaning the remaining 60% of

data analysts, job listings,

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don't list the salary.

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Don't mention the salary at

all, which is a huge bummer.

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Now, some states in the United States

require the job poster to actually say

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what the salary range is, but many don't.

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And hopefully in the future it'll

be a requirement to actually have

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the salary listed, because otherwise

you're wasting job hunter's times,

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and honestly, you're wasting the

hiring manager's times as well.

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It's just better if we can be

transparent and make sure that we

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know what we're applying for and what

you're actually expecting from us.

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Now, to show you all the nitty

gritty on the statistics of what

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these different salaries are,

we're looking at salary, right?

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That is a quantitative variable.

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Basically a number.

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It ranges from zero to, I don't know,

$10 million theoretically, right?

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And we're looking at a

quantitative variable.

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We often wanna look at what's called

the distribution of that numeric

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value Distribution is basically.

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The shape of the data or the shape of that

column, or the shape of that, you know,

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field or whatever you wanna call it, to

show a shape of a data or a distribution.

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You'd often use what's called a

histogram, where you basically create

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these little bins of the value and you

count how many jobs go into those bins,

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and then you stack a bar basically

on how many counts are in there.

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Now I think histograms are great, but I

also think they're a little bit boring.

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And so what I did was I actually created

this raincloud chart right here, which

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works very similar to a histogram, but

I think it looks a little bit cooler and

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gives us a little bit more of an insight.

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So let me explain how this graph works.

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We're actually showing

distribution in three unique ways.

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First, we have basically a histogram

up here on the top, but instead

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of using bars, it's smoothed over.

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This is called a kernel density

estimator or ridge line.

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I like to call it ridge line.

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I think it's a cooler name 'cause it

kind of looks like a mountain or a hill.

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And basically wherever you are on

the x axis, the higher this is,

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the more jobs that have a salary

that fits around right there.

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So, for example, you know

our median is around $92,000.

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That would probably be about right

here, and that's why you see the peak.

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So for example, you see

that our median is $92,000.

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It'd be about right here, and

that's kind of why you see a

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big peak around that space.

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A decent amount of the jobs pay

around 92,000 on average, where

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that mountain is a little bit

lower towards the higher ends.

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This is because there's not many

jobs that pay, you know, around

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$177,000 below this ridge line.

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You have a dot.

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Each one of these dots represents

a data analyst job listing.

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The dots are basically placed at

their salary and if their multiple

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jobs have the same salary, they're

stacked on top of each other.

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So this is basically like an upside

down histogram, but instead of

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using bars, we are using dots.

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This is kind of what's called a B

swarm plot, and I really like it

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'cause it lets you see, you know, the

nitty gritty, you know, all 3000 ish.

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Of these different dots on the page at

once, and then at the bottom we have

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a classic box in whisker box plot that

shows us the median right here, the first

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quartile or the 25 percentile here, the

third quartile or the 75 percentile here.

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And then you have a whisker on both ends

with outliers over here on the right.

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All three of this.

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Are showing the exact same thing.

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Basically the distribution of data analyst

jobs, they call it a rain cloud chart

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because you have this kind of mountain

on top with a bunch of dots below it.

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This is the cloud, and these are

the raindrops falling to a flat line

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on the bottom, which we call Earth.

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That's why it's called

the rain cloud chart.

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Now, I wanna dive into

some of these jobs and.

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See, you know, why they're

paying so high or paying so low.

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But before we do, if you like charts

like this, you like data like this and

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you want more of it, then you should

definitely sign up for my newsletter.

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It's 100% free.

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You can go to data career

jumpstart.com/newsletter,

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or there'll be a nice short

link down below to sign up.

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I send cool charts like this.

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I send data jobs every week

and data insights like the

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salary is $92,000 on average.

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And if that stuff that you think is going

to help you in your career, which is

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stuff I think is gonna help you in your

career, you should definitely sign up.

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Alright, let's go ahead and dive

into some of these lower paying

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jobs and high higher paying jobs.

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Let's start with the lower.

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So for the lower paying jobs, we

have this job right here, which

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I think is quite interesting.

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If you click on the link, it'll

actually open up in a new window.

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Now this job is actually expired, but

we keep the description 'cause we can

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learn a lot from, this is actually

a part-time job that's 15 to $19 per

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hour and the hourly wage is $14 an

hour, which is about $28,000 a year.

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Fun little fact, if you take

your hourly rate and you

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basically, um, multiply it by.

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Two, that's the amount of thousands

of dollars you make as salary.

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So 14 times two is about 28, and

that's why it's $28,000 a year.

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Now, if you actually look closely

at this, this is only for people

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who are enrolled at Enzyme College.

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So not a really good fit for most

of you guys watching probably, but

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I think we can still learn from it.

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I think the low end of the job is 28,000.

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I mean, that's super low in the

us um, but it is for college

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students, so it kind of makes sense.

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It looks like the responsibilities would

be to create dashboards using Power

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bi, Excel, Smartsheets, power Automate

Co-Pilot Studio, and API Connectors.

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So honestly, this is a pretty

advanced, um, role, uh, because it,

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you need to use API connectors and

some of these other tools that I'm

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not even sure a hundred percent what.

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All these are, and that's

why we gave it a mid-level.

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We actually said this was

a mid-level six outta 10.

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I think that's a little bit high.

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It probably should be closer to a four,

and I would still count it as entry

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level because it is an internship,

but that's pretty interesting.

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Um, we can go back over here and also take

a look at some of these lower paying jobs.

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A behavior data specialist, a data

specialist and a data specialist.

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And I include data specialist jobs

on this website because they're kind

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of like a step below a data analyst.

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Oftentimes these roles,

let's open up one of these.

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A lot of these are in, looks

like Kentucky, I guess.

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This one's in Maryland.

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A lot of the times these

roles are pretty simple.

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So let's click on one of these roles.

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Maybe this one right here.

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This is a data specialist for Kentucky

Community and Technical College.

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It looks like this one is still

open and the pays about $34,000.

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That's the salary right there.

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I like these jobs because they

often don't require all that much.

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Right?

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So like you need to have an

associate's degree, which is fine.

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Right?

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Uh, just a little bit of

college experience basically.

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And it looks like you're mostly doing.

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Tracking and analyzing data, we didn't

even capture any skills that it mentions.

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And so usually the, the barrier to entry

for these data specialist roles quite

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a bit lower than like a data analyst.

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Obviously they pay less than a data

analyst, um, but they can be like a

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great entry level data analyst type role.

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Okay, let's go to some of

the higher paying jobs.

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Over here on the right, we can start

with this business intelligence engineer

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role that pays about $204,000 per year.

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Let's take a look at that.

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It is remote, which is pretty awesome.

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Um, we'll talk about more about remote

and hybrid and in person here in a second.

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It is for a company called RTX,

that is an aerospace and defense

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company that provides advanced

system and services for commercial,

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military and government customers.

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So we're kind of in military

government space, and it looks like

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you would be a technical subject

matter expert for the Microsoft data

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and analytics stack with secondary

skills in Databricks and Snowflake.

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So.

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Yeah, basically you'd be using Power

bi, power Query, dax, Microsoft

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Power Platform, um, as well as

doing some stuff with Snowflake,

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Databricks, and SQL based systems.

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And you can see we captured that

this requires Python, SQL, power, bi

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Spark, snowflake and Databricks, or

at least those were the things that

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were mentioned in the job description.

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Now we rated this a nine outta 10

on the senior level, so it is a

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pretty senior level role, and you'll

notice that some of these roles.

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That are high paying, are more senior

and require a little bit more complicated

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tools, like of course they're still gonna

require Python, SQL, and Power bi, but

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then Spark, snowflake and Databricks

are a little bit harder to get access

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to, a little bit harder to learn.

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And so they are kind of reserved for

these more high-end, high paying roles.

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Okay.

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I found another one I

think is interesting.

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It's this, uh, data analyst role at.

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Y Ernest and Young, I guess.

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Right?

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And it looks like the

salary's about $174,000.

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We actually ranked it only a

five out of 10 on seniority.

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Let's see if we can figure

out if we agree or not.

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Uh, bachelor's degree in

some technical fields.

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That's, that's how I

read this, by the way.

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I know it says like all these specific

fields, but I just kind of look

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at, you know, a bachelor's degree.

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That's good.

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Or I guess it has a master's degree

with, uh, four years of experience.

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So this is still kind of mid, it

is looking for years of combined

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experience with these different things.

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It requires SQL, spark, AWS,

Azure, snowflake, and Databricks.

224

00:10:02,255 --> 00:10:04,535

So, yeah, we'll, we'll cover this

here in a second while how these

225

00:10:04,535 --> 00:10:07,835

different salaries depend on these

different skills mentioned, but these

226

00:10:07,835 --> 00:10:10,115

more tough skills like Azure A w.

227

00:10:10,360 --> 00:10:14,380

Cloud-based, infrastructure based, coding

based stuff is really probably going to

228

00:10:14,380 --> 00:10:16,210

get you kind of these higher paying jobs.

229

00:10:16,240 --> 00:10:18,130

Now, let's go ahead and

break this down a little bit.

230

00:10:18,130 --> 00:10:19,990

Let's go ahead and look

at the experience level.

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00:10:20,020 --> 00:10:23,260

So if we look at the experience

level, we see something that maybe

232

00:10:23,260 --> 00:10:26,710

isn't super surprising that entry

level jobs pay the lowest at a

233

00:10:26,710 --> 00:10:29,350

median salary of $76,000 per year.

234

00:10:29,380 --> 00:10:34,000

Mid-level is next at a median salary of

$90,000 per year, and a senior level role.

235

00:10:34,035 --> 00:10:36,735

Pays the most at $113,000 per year.

236

00:10:36,765 --> 00:10:37,844

Now, that's not really surprising.

237

00:10:37,844 --> 00:10:40,635

The more experience you have,

the more you'd expect to get

238

00:10:40,635 --> 00:10:41,865

paid theoretically, right?

239

00:10:41,895 --> 00:10:44,865

However, what I will tell you is

that we do have a lot of overlap

240

00:10:44,895 --> 00:10:46,064

in the distributions, right?

241

00:10:46,094 --> 00:10:49,964

Like for example, there is a decent amount

of height entry level over, you know,

242

00:10:49,964 --> 00:10:52,545

around the median of the senior level.

243

00:10:52,545 --> 00:10:56,775

So there are some entry level data jobs

that pay over six figures for sure.

244

00:10:56,775 --> 00:10:59,025

Even though the median's only $76,000.

245

00:10:59,085 --> 00:11:02,565

And there are some senior roles that

even pay below the entry level median.

246

00:11:02,595 --> 00:11:05,745

So like for instance, this senior

financial analyst role at Amazon

247

00:11:05,775 --> 00:11:11,145

somehow apparently only pays $60,000,

which is, you know, $16,000 below the

248

00:11:11,145 --> 00:11:12,735

median for an entry level data drop.

249

00:11:12,735 --> 00:11:15,285

So there's more that goes into

how much you get paid than

250

00:11:15,285 --> 00:11:17,685

just what your experience level

and your entry level, right?

251

00:11:17,685 --> 00:11:21,015

So there's more that goes into how you

get paid than just your experience level.

252

00:11:21,045 --> 00:11:23,565

You can be entry level and

be making more than someone.

253

00:11:23,805 --> 00:11:26,204

Whose senior level, and that's

actually really, you know,

254

00:11:26,234 --> 00:11:27,734

counterintuitive to a lot of people.

255

00:11:27,885 --> 00:11:29,925

But there's a lot of factors

that we're gonna dive into.

256

00:11:29,954 --> 00:11:31,964

One of the most important

ones is the location.

257

00:11:31,995 --> 00:11:36,074

So for example, if we go to some of these

higher paying jobs in entry level, yeah.

258

00:11:36,074 --> 00:11:38,324

Like for instance, Palo

Alto or New York, right?

259

00:11:38,474 --> 00:11:41,984

It's expensive to live in New York,

it's expensive to live it in California.

260

00:11:41,984 --> 00:11:45,405

And so in order to be competitive,

they have to raise those rates, even

261

00:11:45,405 --> 00:11:48,645

though those are maybe entry level

type jobs versus some of these senior

262

00:11:48,645 --> 00:11:50,234

levels, like this senior data analyst.

263

00:11:50,300 --> 00:11:53,209

Ah, this is in California too, but

there's a lot of factors that go into it.

264

00:11:53,209 --> 00:11:54,650

Let's go ahead and explore another one.

265

00:11:54,709 --> 00:11:56,870

Next one I wanna explore is

actually called the job role.

266

00:11:56,900 --> 00:12:00,140

And this might be kind of controversial,

but I'm, my definition of data

267

00:12:00,140 --> 00:12:03,890

analyst is that you are analyzing

data to improve an organization.

268

00:12:03,920 --> 00:12:07,010

And so I think there's a lot of

families or a lot of job titles that

269

00:12:07,010 --> 00:12:08,064

fall into the data analyst family.

270

00:12:08,805 --> 00:12:11,625

Marketing analysts, financial

analysts, business analysts, BI

271

00:12:11,625 --> 00:12:12,885

engineer, analytics engineer.

272

00:12:12,915 --> 00:12:14,775

Now are some of these roles

a little bit different?

273

00:12:14,775 --> 00:12:15,195

Sure.

274

00:12:15,195 --> 00:12:18,315

But I kind of consider them

roughly all to be data analyst.

275

00:12:18,315 --> 00:12:18,825

D roles.

276

00:12:18,855 --> 00:12:22,665

So the marketing analyst is actually

the lowest at an average of 88,000,

277

00:12:22,665 --> 00:12:25,125

followed by financial analysts at 93,000.

278

00:12:25,185 --> 00:12:28,605

Uh, data analyst, just like

strictly data analyst is 95,000.

279

00:12:28,605 --> 00:12:29,715

And this one's really surprising.

280

00:12:29,755 --> 00:12:34,345

Business analyst was at 99,000 on

average, followed by the bi slash

281

00:12:34,345 --> 00:12:36,745

analytics engineer at $105,000.

282

00:12:36,805 --> 00:12:39,595

I thought the business analyst was pretty

interesting because business analyst

283

00:12:39,595 --> 00:12:43,615

to me is actually like a little bit

easier to get than a data analyst role

284

00:12:43,645 --> 00:12:48,445

because a lot of the times you're not

needing to be necessarily a data expert.

285

00:12:48,445 --> 00:12:50,455

You're more like a business

expert who happens.

286

00:12:50,505 --> 00:12:52,545

To, you know, data capabilities.

287

00:12:52,575 --> 00:12:54,645

So I would really have thought that

that would've been a little bit

288

00:12:54,645 --> 00:12:56,205

lower on average than a data analyst.

289

00:12:56,235 --> 00:12:58,845

But for at least our data

set, it's a little bit higher.

290

00:12:58,845 --> 00:12:59,655

So I thought that was interesting.

291

00:12:59,655 --> 00:13:02,295

'cause a lot of these, you know, if

we go look at one of these roles, I'm

292

00:13:02,295 --> 00:13:05,085

gonna randomly click on one of these

and this is always an adventure when

293

00:13:05,085 --> 00:13:06,525

we're randomly clicking on things.

294

00:13:06,525 --> 00:13:08,535

For instance, this just

is Excel and Power bi.

295

00:13:08,564 --> 00:13:11,835

It's nothing too crazy in terms of what

skills you have to have a bachelor's

296

00:13:11,835 --> 00:13:13,755

degree, three to five years of experience.

297

00:13:13,785 --> 00:13:16,725

We rated it a seven out of 10 on mid,

I think that's even a little bit high.

298

00:13:16,770 --> 00:13:20,970

But like this job right here

is, is nothing super crazy and

299

00:13:21,000 --> 00:13:24,329

has a a low salary, but there's

also gonna be high paying ones.

300

00:13:24,329 --> 00:13:25,199

So it's just interesting.

301

00:13:25,260 --> 00:13:28,800

I will say that this bi slash analytic

analytics engineer being the higher

302

00:13:28,800 --> 00:13:31,290

paying one goes back to what I said

earlier, but like the more senior

303

00:13:31,290 --> 00:13:34,560

roles, once you're doing more coding,

more infrastructure, that often is

304

00:13:34,560 --> 00:13:35,970

reflected with a higher paid salary.

305

00:13:36,000 --> 00:13:38,220

'cause that stuff's hard to

do and really important to do.

306

00:13:38,220 --> 00:13:38,670

Right now.

307

00:13:38,670 --> 00:13:40,650

Let's go ahead and look

at the work arrangement.

308

00:13:41,145 --> 00:13:43,605

Onsite versus remote versus hybrid.

309

00:13:43,635 --> 00:13:45,615

And this is something that

I think is very interesting.

310

00:13:45,615 --> 00:13:49,454

And before I actually get too into it,

I wanna just highlight that everyone

311

00:13:49,454 --> 00:13:50,925

wants a remote data job, and I get it.

312

00:13:50,925 --> 00:13:52,064

I love working remote.

313

00:13:52,125 --> 00:13:55,425

I would say 95% of us

want remote work, right?

314

00:13:55,454 --> 00:13:59,595

However, that's kind of a problem

because it definitely is not 95%

315

00:13:59,595 --> 00:14:00,795

of the data roles that are remote.

316

00:14:00,795 --> 00:14:04,635

In fact, if you come up here to resources

and you go to the remote versus.

317

00:14:04,690 --> 00:14:08,650

Hybrid versus onsite report,

you'll be able to see that only

318

00:14:08,650 --> 00:14:11,830

about 15% of data jobs are remote.

319

00:14:11,890 --> 00:14:18,040

23% of them are hybrid and 63% of them,

two thirds of them basically are onsite.

320

00:14:18,070 --> 00:14:21,310

And this is a really interesting problem

because let's just say, I dunno, 80%

321

00:14:21,310 --> 00:14:23,170

of us want to be working remotely.

322

00:14:23,200 --> 00:14:26,650

Well, that means, uh, a lot of us

are going to be, uh, upset because

323

00:14:26,650 --> 00:14:28,535

there's only 15% of data jobs.

324

00:14:28,890 --> 00:14:30,150

Available that are remote.

325

00:14:30,180 --> 00:14:32,939

And that's where I really

like hybrid, because hybrid is

326

00:14:32,939 --> 00:14:34,920

basically remote in a lot of times.

327

00:14:34,920 --> 00:14:35,220

Right?

328

00:14:35,340 --> 00:14:37,260

Like, what if I said that you

only had to come to the office

329

00:14:37,260 --> 00:14:39,030

once a week that's 80% remote.

330

00:14:39,060 --> 00:14:42,720

Obviously hybrid's a spectrum, but

there is, it's a lot less competitive

331

00:14:42,720 --> 00:14:45,960

because you know, people really want the

remote jobs and there's actually more

332

00:14:45,960 --> 00:14:47,910

hybrid jobs than there are remote jobs.

333

00:14:47,970 --> 00:14:48,975

Anyways, back to the Sal.

334

00:14:49,325 --> 00:14:53,465

Onsite actually pays the lowest, which

I thought was really interesting.

335

00:14:53,495 --> 00:14:56,465

I would really want to go

back into this data and really

336

00:14:56,465 --> 00:14:57,665

thoroughly double check it.

337

00:14:57,665 --> 00:14:59,675

I mean, all these curves

look pretty much the same.

338

00:14:59,675 --> 00:15:03,335

The, the, the median salary for

onsite is 90, for remote it's 95 and

339

00:15:03,335 --> 00:15:07,895

hybrid it's 95 as well, but slightly

a little bit more, uh, skewing right

340

00:15:08,015 --> 00:15:09,665

to, to make it a little bit higher.

341

00:15:09,695 --> 00:15:12,545

To me, this means, you know, you

guys should really chase hybrid

342

00:15:12,545 --> 00:15:13,690

roles because they pay the most.

343

00:15:14,010 --> 00:15:17,069

And I think they're actually

not as competitive as remote.

344

00:15:17,100 --> 00:15:20,189

They might be a little bit more

competitive than onsite, but still

345

00:15:20,220 --> 00:15:22,980

working at least a little bit from

home is awesome, and I think everyone

346

00:15:22,980 --> 00:15:23,910

should have the chance to do it.

347

00:15:23,910 --> 00:15:27,270

So personally, if I was advising you,

I'd say go for these hybrid roles, but

348

00:15:27,270 --> 00:15:30,780

for the most part, it doesn't look like

it affects your salary all that much.

349

00:15:30,810 --> 00:15:33,569

So I guess go for whatever

ones you think you can land.

350

00:15:33,600 --> 00:15:35,910

Next, I wanna show you how

location makes a difference.

351

00:15:36,265 --> 00:15:40,555

As we talked about earlier, you'd expect

if you work in more of a cheap state

352

00:15:40,555 --> 00:15:43,194

to get paid a little bit less versus a

more expensive state like California,

353

00:15:43,194 --> 00:15:44,935

New York, to get paid a little bit more.

354

00:15:44,995 --> 00:15:49,255

So it looks like at the bottom we

have Arizona at 77,000 South Carolina

355

00:15:49,255 --> 00:15:54,655

at 78, Oregon, 78, and my Utah, oh

no, as the fourth lowest paying place

356

00:15:54,655 --> 00:15:56,830

to be a date analyst at $80,000.

357

00:15:57,155 --> 00:15:59,405

Followed by Pennsylvania, $80,000.

358

00:15:59,435 --> 00:16:01,595

Now I will say the sample size

for these is extremely low.

359

00:16:01,595 --> 00:16:04,564

Like for instance, South Carolina,

we only have nine jobs, so it's not

360

00:16:04,564 --> 00:16:07,295

necessarily statistically significant,

but just kind of fun to look at.

361

00:16:07,295 --> 00:16:10,385

And as we continue to post more jobs,

we'll this data will be updated Next.

362

00:16:10,385 --> 00:16:16,084

We have California at a hundred thousand,

Indiana at 102,000, Arkansas at 103,000.

363

00:16:16,084 --> 00:16:17,135

That is shocking.

364

00:16:17,135 --> 00:16:19,040

And obviously a small

sample size of only five.

365

00:16:19,380 --> 00:16:22,050

Virginia at 131 in Nova Scotia.

366

00:16:22,050 --> 00:16:25,800

Small sample size, but

$127,000 as the median.

367

00:16:25,860 --> 00:16:29,130

Uh, what I notice here is that California

and Virginia are probably only two

368

00:16:29,130 --> 00:16:32,250

that have statistically significant

data to actually say they pay a bunch.

369

00:16:32,490 --> 00:16:34,170

California, it's expensive place to live.

370

00:16:34,170 --> 00:16:37,380

There's also a bunch of tech companies

like Google and Tesla and all these

371

00:16:37,380 --> 00:16:38,430

other companies or whatever, right?

372

00:16:38,460 --> 00:16:41,730

And Virginia has a lot of military

and government contractors and it's

373

00:16:41,730 --> 00:16:42,960

also an expensive place to live.

374

00:16:42,990 --> 00:16:45,000

'cause DC's kind of basically right there.

375

00:16:45,065 --> 00:16:48,425

The last thing I wanna break down

for these salaries is what skills

376

00:16:48,425 --> 00:16:50,825

are mentioned and what you can

kind of get paid based off of.

377

00:16:50,825 --> 00:16:51,335

What skills.

378

00:16:51,335 --> 00:16:51,635

You know.

379

00:16:51,665 --> 00:16:57,515

The bottom skill is Excel at 88,000,

followed by Power BI at 96,000.

380

00:16:57,515 --> 00:17:01,715

Tableau, 99,000 sql a

hundred thousand AWS.

381

00:17:01,760 --> 00:17:09,348

102,000 Python 102,000 R,

106,000, Azure 110,000 Snowflake.

382

00:17:09,348 --> 00:17:14,240

A whopping 1 21 K,

followed by DBT at 131 K.

383

00:17:14,300 --> 00:17:16,160

So what can we learn from this?

384

00:17:16,190 --> 00:17:21,560

I think basically what I take away is the

easier a skill is to learn and easier a

385

00:17:21,560 --> 00:17:24,530

skill is to, or a tool is to actually use.

386

00:17:24,575 --> 00:17:28,505

The lower the salary expectation is,

for example, we've all learned Excel.

387

00:17:28,505 --> 00:17:31,295

We've all used Excel a little bit,

and it's not hard to learn how

388

00:17:31,295 --> 00:17:34,955

to analyze data in Excel, and so

that's why you know it's the lowest

389

00:17:35,015 --> 00:17:36,875

data tool, lowest paying data tool.

390

00:17:36,905 --> 00:17:38,435

Next, there's power behind Tableau.

391

00:17:38,495 --> 00:17:40,715

These are your business

intelligence dashboard tools.

392

00:17:40,745 --> 00:17:42,905

These aren't super complicated

to get started with.

393

00:17:42,935 --> 00:17:45,035

If you can figure out how

to make a PowerPoint slide.

394

00:17:45,205 --> 00:17:48,745

You can figure out how to create a

dashboard in Power BI in Tableau, it's,

395

00:17:48,774 --> 00:17:50,665

you know, click, it's drag and drop.

396

00:17:50,665 --> 00:17:51,985

It's basically click-based.

397

00:17:52,014 --> 00:17:52,915

No scripting.

398

00:17:52,915 --> 00:17:55,435

Although there is scripting in both of

them, they can get pretty complicated.

399

00:17:55,435 --> 00:17:57,145

But to get started, um,

they're pretty simple.

400

00:17:57,205 --> 00:18:00,625

Next, you kinda have the sql, Python,

and R group, and these are the

401

00:18:00,685 --> 00:18:04,764

languages, um, things that you have to

code and that takes a lot more time to

402

00:18:04,764 --> 00:18:06,415

learn and a lot more time to perfect.

403

00:18:06,475 --> 00:18:08,695

So that's why they get paid a

little bit more, followed by.

404

00:18:08,725 --> 00:18:12,925

Lastly, this cohort of AWS

Azure, snowflake, and DBT.

405

00:18:12,955 --> 00:18:14,365

This is more cloud-based.

406

00:18:14,385 --> 00:18:20,445

Infrastructure systems type stuff, that's

one hard to learn and two hard to do well.

407

00:18:20,475 --> 00:18:23,925

And then three really important to make

sure everything's working correctly.

408

00:18:24,015 --> 00:18:27,315

Um, 'cause this is more like critical

infrastructure as opposed to just

409

00:18:27,315 --> 00:18:28,575

kind of maybe some analytics.

410

00:18:28,605 --> 00:18:33,315

So I still think that Excel Power, bi,

Tableau, SQL, are the easiest data tools.

411

00:18:33,370 --> 00:18:36,370

To learn the fastest and

also the most in demand.

412

00:18:36,370 --> 00:18:37,750

So this is probably where I'd start.

413

00:18:37,750 --> 00:18:40,209

And then once you get more into,

once you've learned those, and then

414

00:18:40,209 --> 00:18:42,669

once you've learned those, you can

get into more of the specialty tools

415

00:18:42,669 --> 00:18:46,060

like AWS or R or Azure or Snowflake,

and that's what's going to actually

416

00:18:46,060 --> 00:18:48,129

help you get paid more in the end.

417

00:18:48,189 --> 00:18:51,370

Alright, so I'm hoping all

this salary data made you more

418

00:18:51,370 --> 00:18:52,840

informed with all these numbers.

419

00:18:52,840 --> 00:18:55,629

And remember that numbers equals

knowledge, and knowledge is

420

00:18:55,629 --> 00:18:57,455

power, and power is confidence.

421

00:18:57,755 --> 00:18:59,435

So be more confident.

422

00:18:59,465 --> 00:19:01,115

You know what you can expect salary wise.

423

00:19:01,115 --> 00:19:03,785

Now you know what you need to chase

after, what skills you need to learn,

424

00:19:03,785 --> 00:19:04,985

what roles you need to go after.

425

00:19:05,015 --> 00:19:07,385

Now be confident and go

out there and get it.

426

00:19:07,415 --> 00:19:10,325

I mean, that's exactly why

I built find a data job.com

427

00:19:10,355 --> 00:19:13,475

is to help people like you

confidently land data jobs.

428

00:19:13,505 --> 00:19:15,725

So make sure you check it

out, links to the description.

429

00:19:15,755 --> 00:19:18,695

Thank you for watching or listening,

and I'll see you in the next one.

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