208: I Analyzed 8,553 Data Analyst Salaries — Here's What They're ACTUALLY Paying in 2026

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

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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.

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

here in a second while how these

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different salaries depend on these

different skills mentioned, but these

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more tough skills like Azure A w.

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Cloud-based, infrastructure based, coding

based stuff is really probably going to

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get you kind of these higher paying jobs.

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Now, let's go ahead and

break this down a little bit.

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Let's go ahead and look

at the experience level.

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So if we look at the experience

level, we see something that maybe

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isn't super surprising that entry

level jobs pay the lowest at a

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median salary of $76,000 per year.

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Mid-level is next at a median salary of

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

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Pays the most at $113,000 per year.

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Now, that's not really surprising.

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The more experience you have,

the more you'd expect to get

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paid theoretically, right?

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However, what I will tell you is

that we do have a lot of overlap

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in the distributions, right?

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Like for example, there is a decent amount

of height entry level over, you know,

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around the median of the senior level.

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So there are some entry level data jobs

that pay over six figures for sure.

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

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And there are some senior roles that

even pay below the entry level median.

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So like for instance, this senior

financial analyst role at Amazon

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somehow apparently only pays $60,000,

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

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median for an entry level data drop.

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So there's more that goes into

how much you get paid than

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just what your experience level

and your entry level, right?

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So there's more that goes into how you

get paid than just your experience level.

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You can be entry level and

be making more than someone.

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Whose senior level, and that's

actually really, you know,

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counterintuitive to a lot of people.

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But there's a lot of factors

that we're gonna dive into.

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One of the most important

ones is the location.

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So for example, if we go to some of these

higher paying jobs in entry level, yeah.

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Like for instance, Palo

Alto or New York, right?

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It's expensive to live in New York,

it's expensive to live it in California.

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And so in order to be competitive,

they have to raise those rates, even

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though those are maybe entry level

type jobs versus some of these senior

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levels, like this senior data analyst.

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Ah, this is in California too, but

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

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Let's go ahead and explore another one.

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Next one I wanna explore is

actually called the job role.

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And this might be kind of controversial,

but I'm, my definition of data

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analyst is that you are analyzing

data to improve an organization.

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And so I think there's a lot of

families or a lot of job titles that

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fall into the data analyst family.

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Marketing analysts, financial

analysts, business analysts, BI

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engineer, analytics engineer.

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Now are some of these roles

a little bit different?

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Sure.

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But I kind of consider them

roughly all to be data analyst.

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D roles.

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So the marketing analyst is actually

the lowest at an average of 88,000,

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followed by financial analysts at 93,000.

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Uh, data analyst, just like

strictly data analyst is 95,000.

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And this one's really surprising.

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Business analyst was at 99,000 on

average, followed by the bi slash

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analytics engineer at $105,000.

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I thought the business analyst was pretty

interesting because business analyst

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to me is actually like a little bit

easier to get than a data analyst role

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because a lot of the times you're not

needing to be necessarily a data expert.

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You're more like a business

expert who happens.

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To, you know, data capabilities.

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So I would really have thought that

that would've been a little bit

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lower on average than a data analyst.

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But for at least our data

set, it's a little bit higher.

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So I thought that was interesting.

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'cause a lot of these, you know, if

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

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gonna randomly click on one of these

and this is always an adventure when

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we're randomly clicking on things.

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For instance, this just

is Excel and Power bi.

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It's nothing too crazy in terms of what

skills you have to have a bachelor's

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degree, three to five years of experience.

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We rated it a seven out of 10 on mid,

I think that's even a little bit high.

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But like this job right here

is, is nothing super crazy and

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has a a low salary, but there's

also gonna be high paying ones.

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: 00:13:24

So it's just interesting.

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: 00:13:25

I will say that this bi slash analytic

analytics engineer being the higher

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: 00:13:28

paying one goes back to what I said

earlier, but like the more senior

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roles, once you're doing more coding,

more infrastructure, that often is

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: 00:13:34

reflected with a higher paid salary.

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: 00:13:36

'cause that stuff's hard to

do and really important to do.

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: 00:13:38

Right now.

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: 00:13:38

Let's go ahead and look

at the work arrangement.

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: 00:13:41

Onsite versus remote versus hybrid.

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: 00:13:43

And this is something that

I think is very interesting.

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: 00:13:45

And before I actually get too into it,

I wanna just highlight that everyone

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wants a remote data job, and I get it.

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: 00:13:50

I love working remote.

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: 00:13:52

I would say 95% of us

want remote work, right?

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: 00:13:55

However, that's kind of a problem

because it definitely is not 95%

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: 00:13:59

of the data roles that are remote.

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: 00:14:00

In fact, if you come up here to resources

and you go to the remote versus.

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Hybrid versus onsite report,

you'll be able to see that only

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: 00:14:08

about 15% of data jobs are remote.

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: 00:14:11

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

two thirds of them basically are onsite.

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: 00:14:18

And this is a really interesting problem

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

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: 00:14:21

of us want to be working remotely.

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: 00:14:23

Well, that means, uh, a lot of us

are going to be, uh, upset because

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: 00:14:26

there's only 15% of data jobs.

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: 00:14:28

Available that are remote.

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: 00:14:30

And that's where I really

like hybrid, because hybrid is

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: 00:14:32

basically remote in a lot of times.

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: 00:14:34

Right?

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: 00:14:35

Like, what if I said that you

only had to come to the office

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: 00:14:37

once a week that's 80% remote.

330

: 00:14:39

Obviously hybrid's a spectrum, but

there is, it's a lot less competitive

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: 00:14:42

because you know, people really want the

remote jobs and there's actually more

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: 00:14:45

hybrid jobs than there are remote jobs.

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: 00:14:47

Anyways, back to the Sal.

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: 00:14:49

Onsite actually pays the lowest, which

I thought was really interesting.

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: 00:14:53

I would really want to go

back into this data and really

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: 00:14:56

thoroughly double check it.

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: 00:14:57

I mean, all these curves

look pretty much the same.

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: 00:14:59

The, the, the median salary for

onsite is 90, for remote it's 95 and

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: 00:15:03

hybrid it's 95 as well, but slightly

a little bit more, uh, skewing right

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: 00:15:08

to, to make it a little bit higher.

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: 00:15:09

To me, this means, you know, you

guys should really chase hybrid

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: 00:15:12

roles because they pay the most.

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: 00:15:14

And I think they're actually

not as competitive as remote.

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: 00:15:17

They might be a little bit more

competitive than onsite, but still

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: 00:15:20

working at least a little bit from

home is awesome, and I think everyone

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: 00:15:22

should have the chance to do it.

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: 00:15:23

So personally, if I was advising you,

I'd say go for these hybrid roles, but

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: 00:15:27

for the most part, it doesn't look like

it affects your salary all that much.

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: 00:15:30

So I guess go for whatever

ones you think you can land.

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: 00:15:33

Next, I wanna show you how

location makes a difference.

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: 00:15:36

As we talked about earlier, you'd expect

if you work in more of a cheap state

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: 00:15:40

to get paid a little bit less versus a

more expensive state like California,

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: 00:15:43

New York, to get paid a little bit more.

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: 00:15:44

So it looks like at the bottom we

have Arizona at 77,000 South Carolina

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: 00:15:49

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

no, as the fourth lowest paying place

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: 00:15:54

to be a date analyst at $80,000.

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: 00:15:57

Followed by Pennsylvania, $80,000.

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: 00:15:59

Now I will say the sample size

for these is extremely low.

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: 00:16:01

Like for instance, South Carolina,

we only have nine jobs, so it's not

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: 00:16:04

necessarily statistically significant,

but just kind of fun to look at.

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: 00:16:07

And as we continue to post more jobs,

we'll this data will be updated Next.

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: 00:16:10

We have California at a hundred thousand,

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

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: 00:16:16

That is shocking.

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: 00:16:17

And obviously a small

sample size of only five.

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: 00:16:19

Virginia at 131 in Nova Scotia.

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: 00:16:22

Small sample size, but

$127,000 as the median.

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: 00:16:25

Uh, what I notice here is that California

and Virginia are probably only two

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: 00:16:29

that have statistically significant

data to actually say they pay a bunch.

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: 00:16:32

California, it's expensive place to live.

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: 00:16:34

There's also a bunch of tech companies

like Google and Tesla and all these

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: 00:16:37

other companies or whatever, right?

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: 00:16:38

And Virginia has a lot of military

and government contractors and it's

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: 00:16:41

also an expensive place to live.

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: 00:16:42

'cause DC's kind of basically right there.

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: 00:16:45

The last thing I wanna break down

for these salaries is what skills

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: 00:16:48

are mentioned and what you can

kind of get paid based off of.

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: 00:16:50

What skills.

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: 00:16:51

You know.

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: 00:16:51

The bottom skill is Excel at 88,000,

followed by Power BI at 96,000.

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: 00:16:57

Tableau, 99,000 sql a

hundred thousand AWS.

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: 00:17:01

102,000 Python 102,000 R,

106,000, Azure 110,000 Snowflake.

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: 00:17:09

A whopping 1 21 K,

followed by DBT at 131 K.

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: 00:17:14

So what can we learn from this?

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: 00:17:16

I think basically what I take away is the

easier a skill is to learn and easier a

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: 00:17:21

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

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: 00:17:24

The lower the salary expectation is,

for example, we've all learned Excel.

387

: 00:17:28

We've all used Excel a little bit,

and it's not hard to learn how

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: 00:17:31

to analyze data in Excel, and so

that's why you know it's the lowest

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: 00:17:35

data tool, lowest paying data tool.

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: 00:17:36

Next, there's power behind Tableau.

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: 00:17:38

These are your business

intelligence dashboard tools.

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: 00:17:40

These aren't super complicated

to get started with.

393

: 00:17:42

If you can figure out how

to make a PowerPoint slide.

394

: 00:17:45

You can figure out how to create a

dashboard in Power BI in Tableau, it's,

395

: 00:17:48

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

396

: 00:17:50

It's basically click-based.

397

: 00:17:52

No scripting.

398

: 00:17:52

Although there is scripting in both of

them, they can get pretty complicated.

399

: 00:17:55

But to get started, um,

they're pretty simple.

400

: 00:17:57

Next, you kinda have the sql, Python,

and R group, and these are the

401

: 00:18:00

languages, um, things that you have to

code and that takes a lot more time to

402

: 00:18:04

learn and a lot more time to perfect.

403

: 00:18:06

So that's why they get paid a

little bit more, followed by.

404

: 00:18:08

Lastly, this cohort of AWS

Azure, snowflake, and DBT.

405

: 00:18:12

This is more cloud-based.

406

: 00:18:14

Infrastructure systems type stuff, that's

one hard to learn and two hard to do well.

407

: 00:18:20

And then three really important to make

sure everything's working correctly.

408

: 00:18:24

Um, 'cause this is more like critical

infrastructure as opposed to just

409

: 00:18:27

kind of maybe some analytics.

410

: 00:18:28

So I still think that Excel Power, bi,

Tableau, SQL, are the easiest data tools.

411

: 00:18:33

To learn the fastest and

also the most in demand.

412

: 00:18:36

So this is probably where I'd start.

413

: 00:18:37

And then once you get more into,

once you've learned those, and then

414

: 00:18:40

once you've learned those, you can

get into more of the specialty tools

415

: 00:18:42

like AWS or R or Azure or Snowflake,

and that's what's going to actually

416

: 00:18:46

help you get paid more in the end.

417

: 00:18:48

Alright, so I'm hoping all

this salary data made you more

418

: 00:18:51

informed with all these numbers.

419

: 00:18:52

And remember that numbers equals

knowledge, and knowledge is

420

: 00:18:55

power, and power is confidence.

421

: 00:18:57

So be more confident.

422

: 00:18:59

You know what you can expect salary wise.

423

: 00:19:01

Now you know what you need to chase

after, what skills you need to learn,

424

: 00:19:03

what roles you need to go after.

425

: 00:19:05

Now be confident and go

out there and get it.

426

: 00:19:07

I mean, that's exactly why

I built find a data job.com

427

: 00:19:10

is to help people like you

confidently land data jobs.

428

: 00:19:13

So make sure you check it

out, links to the description.

429

: 00:19:15

Thank you for watching or listening,

and I'll see you in the next one.