Help us become the #1 Data Podcast by leaving a rating & review! We are 67 reviews away!

I analyzed 8,554 data analyst salaries. Here's what the market actually looks like right now.

💌 Join 30k+ aspiring data analysts & get my tips in your inbox weekly 👉 https://datacareerjumpstart.com/newsletter

🆘 Feeling stuck in your data journey? Come to my next free "How to Land Your First Data Job" training 👉 https://datacareerjumpstart.com/training

👩‍💻 Want to land a data job in less than 90 days? 👉 https://datacareerjumpstart.com/daa

👔 Ace The Interview with Confidence 👉 https://datacareerjumpstart.com/interviewsimulator

⌚ TIMESTAMPS

⌚ TIMESTAMPS

01:33 – The real median salary

05:57 – Lowest vs highest paying roles

10:18 – Salary by experience level

11:57 – Salary by job title

13:39 – Remote vs hybrid vs onsite

15:33 – Salary by state

16:48 – Salary by skill

18:48 – What I'd do with this

🔗 CONNECT WITH AVERY

🎥 YouTube Channel

🤝 LinkedIn

📸 Instagram

🎵 TikTok

💻 Website

Mentioned in this episode:

May Cohort of the Data Analytics Accelerator — Now Open

🔗 datacareerjumpstart.com/daa The May cohort of the Data Analytics Accelerator is officially open for enrollment. This is my comprehensive data analytics bootcamp that takes you from wherever you are to landing your first data job. Doesn't matter your background, your degree, or your experience level — we're going to help you get there. What you get: 📊 Full curriculum covering Excel, SQL, Tableau, Python, and R 🛠️ 9 real-world projects across different industries to build your portfolio 💼 LinkedIn, resume, and interview prep so you actually stand out to recruiters 🤝 Weekly office hours, coaching, and a community of 900+ aspiring analysts who are in it with you 🎓 Lifetime access — go at your pace, come back anytime May enrollment deal: 🔥 20% off when you enroll now 🎁 6 free months of my unreleased Data Portfolio Builder tool — this isn't publicly available yet, and every May cohort member gets early access The live kickoff call is with yours truly on Monday, May 11th at 7:00 PM Eastern. Make sure you're enrolled before then so you don't miss it. 👉 datacareerjumpstart.com/daa Or just click the link in the show notes down below. See you on May 11th.

https://datacareerjumpstart.com/daa

So I just analyzed 8,554 data analyst

jobs to find out exactly what they

2

are paying right now and the results.

3

They even shocked me.

4

And I look at data, job

listings for a living.

5

So in this episode, I'll break

down 8,000 different salaries.

6

In every way that you possibly can by

experience level, by job title, by remote

7

versus in office, by state and by skill.

8

And I'll show you the highest paid

job and the lowest paid job and

9

exactly what you need to do to land

that $212,000 data analyst role.

10

So let's go ahead and get into it.

11

By the way, if you're new

here, my name is Avery Smith.

12

I'm a senior data analyst with 10 years

of experience, and now I spend all of my

13

time trying to help people like you land.

14

Data jobs and everything that I'm going

to be showing you all the data and all

15

the graphs and all the salaries is going

to be coming from Find a data job.com,

16

which is actually a data analyst

job board where that you can

17

use to find data analyst jobs.

18

It's actually one that I run and we

post dozens of data jobs for free.

19

Online every day that you

guys can apply for right now.

20

So if you haven't bookmarked it

yet, please go ahead and do so.

21

It's a really useful resource

and there'll be a link in the

22

description down below as well.

23

And all the graphs and data I'll

be showing you is available as well

24

in our salary report right here.

25

And we'll have a link to it

in the show notes down below.

26

And if you're listening only via audio

on Spotify or Apple Podcast or something,

27

I'm gonna do my absolute best to narrate

everything that I'm showing today.

28

That way you can basically

picture the graphs in your head.

29

Okay, so let's get to what actually

really shocked me right away, and there's

30

a few things I really wanna highlight.

31

The first one is actually

this median salary.

32

The median salary of the 8,553

jobs I looked at was basically 92.

33

Thousand dollars.

34

And I thought that was pretty impressive

because if you go online and you

35

check like Indeed or Glassdoor,

they're gonna tell you the median

36

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

37

And I'm saying it's about seven to

$10,000 more, which is like what?

38

10 plus percent more.

39

It's not insignificant.

40

Now, of course our data

sets are very different.

41

We have different amount of jobs,

different types of jobs, so on

42

and so forth, so, so it's not

something to get too bogged down

43

in, but I think this is a good sign.

44

At least the jobs that I'm

posting on find data job.com

45

have a little bit higher

salary on general and average.

46

So another reason you guys should

be using find data job.com.

47

The other thing that blew me away is the

stats right here that out of the 8,553

48

jobs that I analyzed, only 3,451 of

them had anything to do with salaries.

49

Had only any mention of

salary or salary data.

50

That's only 40%.

51

Meaning the remaining 60% of

data analysts, job listings,

52

don't list the salary.

53

Don't mention the salary at

all, which is a huge bummer.

54

Now, some states in the United States

require the job poster to actually say

55

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

56

And hopefully in the future it'll

be a requirement to actually have

57

the salary listed, because otherwise

you're wasting job hunter's times,

58

and honestly, you're wasting the

hiring manager's times as well.

59

It's just better if we can be

transparent and make sure that we

60

know what we're applying for and what

you're actually expecting from us.

61

Now, to show you all the nitty

gritty on the statistics of what

62

these different salaries are,

we're looking at salary, right?

63

That is a quantitative variable.

64

Basically a number.

65

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

$10 million theoretically, right?

66

And we're looking at a

quantitative variable.

67

We often wanna look at what's called

the distribution of that numeric

68

value Distribution is basically.

69

The shape of the data or the shape of that

column, or the shape of that, you know,

70

field or whatever you wanna call it, to

show a shape of a data or a distribution.

71

You'd often use what's called a

histogram, where you basically create

72

these little bins of the value and you

count how many jobs go into those bins,

73

and then you stack a bar basically

on how many counts are in there.

74

Now I think histograms are great, but I

also think they're a little bit boring.

75

And so what I did was I actually created

this raincloud chart right here, which

76

works very similar to a histogram, but

I think it looks a little bit cooler and

77

gives us a little bit more of an insight.

78

So let me explain how this graph works.

79

We're actually showing

distribution in three unique ways.

80

First, we have basically a histogram

up here on the top, but instead

81

of using bars, it's smoothed over.

82

This is called a kernel density

estimator or ridge line.

83

I like to call it ridge line.

84

I think it's a cooler name 'cause it

kind of looks like a mountain or a hill.

85

And basically wherever you are on

the x axis, the higher this is,

86

the more jobs that have a salary

that fits around right there.

87

So, for example, you know

our median is around $92,000.

88

That would probably be about right

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

89

So for example, you see

that our median is $92,000.

90

It'd be about right here, and

that's kind of why you see a

91

big peak around that space.

92

A decent amount of the jobs pay

around 92,000 on average, where

93

that mountain is a little bit

lower towards the higher ends.

94

This is because there's not many

jobs that pay, you know, around

95

$177,000 below this ridge line.

96

You have a dot.

97

Each one of these dots represents

a data analyst job listing.

98

The dots are basically placed at

their salary and if their multiple

99

jobs have the same salary, they're

stacked on top of each other.

100

So this is basically like an upside

down histogram, but instead of

101

using bars, we are using dots.

102

This is kind of what's called a B

swarm plot, and I really like it

103

'cause it lets you see, you know, the

104

Of these different dots on the page at

once, and then at the bottom we have

105

a classic box in whisker box plot that

shows us the median right here, the first

106

quartile or the 25 percentile here, the

third quartile or the 75 percentile here.

107

And then you have a whisker on both ends

with outliers over here on the right.

108

All three of this.

109

Are showing the exact same thing.

110

Basically the distribution of data analyst

jobs, they call it a rain cloud chart

111

because you have this kind of mountain

on top with a bunch of dots below it.

112

This is the cloud, and these are

the raindrops falling to a flat line

113

on the bottom, which we call Earth.

114

That's why it's called

the rain cloud chart.

115

Now, I wanna dive into

some of these jobs and.

116

See, you know, why they're

paying so high or paying so low.

117

But before we do, if you like charts

like this, you like data like this and

118

you want more of it, then you should

definitely sign up for my newsletter.

119

It's 100% free.

120

You can go to data career

jumpstart.com/newsletter,

121

or there'll be a nice short

link down below to sign up.

122

I send cool charts like this.

123

I send data jobs every week

and data insights like the

124

salary is $92,000 on average.

125

And if that stuff that you think is going

to help you in your career, which is

126

stuff I think is gonna help you in your

career, you should definitely sign up.

127

Alright, let's go ahead and dive

into some of these lower paying

128

jobs and high higher paying jobs.

129

Let's start with the lower.

130

So for the lower paying jobs, we

have this job right here, which

131

I think is quite interesting.

132

If you click on the link, it'll

actually open up in a new window.

133

Now this job is actually expired, but

we keep the description 'cause we can

134

learn a lot from, this is actually

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

135

hour and the hourly wage is $14 an

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

136

Fun little fact, if you take

your hourly rate and you

137

basically, um, multiply it by.

138

Two, that's the amount of thousands

of dollars you make as salary.

139

So 14 times two is about 28, and

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

140

Now, if you actually look closely

at this, this is only for people

141

who are enrolled at Enzyme College.

142

So not a really good fit for most

of you guys watching probably, but

143

I think we can still learn from it.

144

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

145

I mean, that's super low in the

us um, but it is for college

146

students, so it kind of makes sense.

147

It looks like the responsibilities would

be to create dashboards using Power

148

bi, Excel, Smartsheets, power Automate

Co-Pilot Studio, and API Connectors.

149

So honestly, this is a pretty

advanced, um, role, uh, because it,

150

you need to use API connectors and

some of these other tools that I'm

151

not even sure a hundred percent what.

152

All these are, and that's

why we gave it a mid-level.

153

We actually said this was

a mid-level six outta 10.

154

I think that's a little bit high.

155

It probably should be closer to a four,

and I would still count it as entry

156

level because it is an internship,

but that's pretty interesting.

157

Um, we can go back over here and also take

a look at some of these lower paying jobs.

158

A behavior data specialist, a data

specialist and a data specialist.

159

And I include data specialist jobs

on this website because they're kind

160

of like a step below a data analyst.

161

Oftentimes these roles,

let's open up one of these.

162

A lot of these are in, looks

like Kentucky, I guess.

163

This one's in Maryland.

164

A lot of the times these

roles are pretty simple.

165

So let's click on one of these roles.

166

Maybe this one right here.

167

This is a data specialist for Kentucky

Community and Technical College.

168

It looks like this one is still

open and the pays about $34,000.

169

That's the salary right there.

170

I like these jobs because they

often don't require all that much.

171

Right?

172

So like you need to have an

associate's degree, which is fine.

173

Right?

174

Uh, just a little bit of

college experience basically.

175

And it looks like you're mostly doing.

176

Tracking and analyzing data, we didn't

even capture any skills that it mentions.

177

And so usually the, the barrier to entry

for these data specialist roles quite

178

a bit lower than like a data analyst.

179

Obviously they pay less than a data

analyst, um, but they can be like a

180

great entry level data analyst type role.

181

Okay, let's go to some of

the higher paying jobs.

182

Over here on the right, we can start

with this business intelligence engineer

183

role that pays about $204,000 per year.

184

Let's take a look at that.

185

It is remote, which is pretty awesome.

186

Um, we'll talk about more about remote

and hybrid and in person here in a second.

187

It is for a company called RTX,

that is an aerospace and defense

188

company that provides advanced

system and services for commercial,

189

military and government customers.

190

So we're kind of in military

government space, and it looks like

191

you would be a technical subject

matter expert for the Microsoft data

192

and analytics stack with secondary

skills in Databricks and Snowflake.

193

So.

194

Yeah, basically you'd be using Power

bi, power Query, dax, Microsoft

195

Power Platform, um, as well as

doing some stuff with Snowflake,

196

Databricks, and SQL based systems.

197

And you can see we captured that

this requires Python, SQL, power, bi

198

Spark, snowflake and Databricks, or

at least those were the things that

199

were mentioned in the job description.

200

Now we rated this a nine outta 10

on the senior level, so it is a

201

pretty senior level role, and you'll

notice that some of these roles.

202

That are high paying, are more senior

and require a little bit more complicated

203

tools, like of course they're still gonna

require Python, SQL, and Power bi, but

204

then Spark, snowflake and Databricks

are a little bit harder to get access

205

to, a little bit harder to learn.

206

And so they are kind of reserved for

these more high-end, high paying roles.

207

Okay.

208

I found another one I

think is interesting.

209

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

210

Y Ernest and Young, I guess.

211

Right?

212

And it looks like the

salary's about $174,000.

213

We actually ranked it only a

five out of 10 on seniority.

214

Let's see if we can figure

out if we agree or not.

215

Uh, bachelor's degree in

some technical fields.

216

That's, that's how I

read this, by the way.

217

I know it says like all these specific

fields, but I just kind of look

218

at, you know, a bachelor's degree.

219

That's good.

220

Or I guess it has a master's degree

with, uh, four years of experience.

221

So this is still kind of mid, it

is looking for years of combined

222

experience with these different things.

223

It requires SQL, spark, AWS,

Azure, snowflake, and Databricks.

224

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

here in a second while how these

225

different salaries depend on these

different skills mentioned, but these

226

more tough skills like Azure A w.

227

Cloud-based, infrastructure based, coding

based stuff is really probably going to

228

get you kind of these higher paying jobs.

229

Now, let's go ahead and

break this down a little bit.

230

Let's go ahead and look

at the experience level.

231

So if we look at the experience

level, we see something that maybe

232

isn't super surprising that entry

level jobs pay the lowest at a

233

median salary of $76,000 per year.

234

Mid-level is next at a median salary of

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

235

Pays the most at $113,000 per year.

236

Now, that's not really surprising.

237

The more experience you have,

the more you'd expect to get

238

paid theoretically, right?

239

However, what I will tell you is

that we do have a lot of overlap

240

in the distributions, right?

241

Like for example, there is a decent amount

of height entry level over, you know,

242

around the median of the senior level.

243

So there are some entry level data jobs

that pay over six figures for sure.

244

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

245

And there are some senior roles that

even pay below the entry level median.

246

So like for instance, this senior

financial analyst role at Amazon

247

somehow apparently only pays $60,000,

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

248

median for an entry level data drop.

249

So there's more that goes into

how much you get paid than

250

just what your experience level

and your entry level, right?

251

So there's more that goes into how you

get paid than just your experience level.

252

You can be entry level and

be making more than someone.

253

Whose senior level, and that's

actually really, you know,

254

counterintuitive to a lot of people.

255

But there's a lot of factors

that we're gonna dive into.

256

One of the most important

ones is the location.

257

So for example, if we go to some of these

higher paying jobs in entry level, yeah.

258

Like for instance, Palo

Alto or New York, right?

259

It's expensive to live in New York,

it's expensive to live it in California.

260

And so in order to be competitive,

they have to raise those rates, even

261

though those are maybe entry level

type jobs versus some of these senior

262

levels, like this senior data analyst.

263

Ah, this is in California too, but

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

264

Let's go ahead and explore another one.

265

Next one I wanna explore is

actually called the job role.

266

And this might be kind of controversial,

but I'm, my definition of data

267

analyst is that you are analyzing

data to improve an organization.

268

And so I think there's a lot of

families or a lot of job titles that

269

fall into the data analyst family.

270

Marketing analysts, financial

analysts, business analysts, BI

271

engineer, analytics engineer.

272

Now are some of these roles

a little bit different?

273

Sure.

274

But I kind of consider them

roughly all to be data analyst.

275

D roles.

276

So the marketing analyst is actually

the lowest at an average of 88,000,

277

followed by financial analysts at 93,000.

278

Uh, data analyst, just like

strictly data analyst is 95,000.

279

And this one's really surprising.

280

Business analyst was at 99,000 on

average, followed by the bi slash

281

analytics engineer at $105,000.

282

I thought the business analyst was pretty

interesting because business analyst

283

to me is actually like a little bit

easier to get than a data analyst role

284

because a lot of the times you're not

needing to be necessarily a data expert.

285

You're more like a business

expert who happens.

286

To, you know, data capabilities.

287

So I would really have thought that

that would've been a little bit

288

lower on average than a data analyst.

289

But for at least our data

set, it's a little bit higher.

290

So I thought that was interesting.

291

'cause a lot of these, you know, if

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

292

gonna randomly click on one of these

and this is always an adventure when

293

we're randomly clicking on things.

294

For instance, this just

is Excel and Power bi.

295

It's nothing too crazy in terms of what

skills you have to have a bachelor's

296

degree, three to five years of experience.

297

We rated it a seven out of 10 on mid,

I think that's even a little bit high.

298

But like this job right here

is, is nothing super crazy and

299

has a a low salary, but there's

also gonna be high paying ones.

300

So it's just interesting.

301

I will say that this bi slash analytic

analytics engineer being the higher

302

paying one goes back to what I said

earlier, but like the more senior

303

roles, once you're doing more coding,

more infrastructure, that often is

304

reflected with a higher paid salary.

305

'cause that stuff's hard to

do and really important to do.

306

Right now.

307

Let's go ahead and look

at the work arrangement.

308

Onsite versus remote versus hybrid.

309

And this is something that

I think is very interesting.

310

And before I actually get too into it,

I wanna just highlight that everyone

311

wants a remote data job, and I get it.

312

I love working remote.

313

I would say 95% of us

want remote work, right?

314

However, that's kind of a problem

because it definitely is not 95%

315

of the data roles that are remote.

316

In fact, if you come up here to resources

and you go to the remote versus.

317

Hybrid versus onsite report,

you'll be able to see that only

318

about 15% of data jobs are remote.

319

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

two thirds of them basically are onsite.

320

And this is a really interesting problem

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

321

of us want to be working remotely.

322

Well, that means, uh, a lot of us

are going to be, uh, upset because

323

there's only 15% of data jobs.

324

Available that are remote.

325

And that's where I really

like hybrid, because hybrid is

326

basically remote in a lot of times.

327

Right?

328

Like, what if I said that you

only had to come to the office

329

once a week that's 80% remote.

330

Obviously hybrid's a spectrum, but

there is, it's a lot less competitive

331

because you know, people really want the

remote jobs and there's actually more

332

hybrid jobs than there are remote jobs.

333

Anyways, back to the Sal.

334

Onsite actually pays the lowest, which

I thought was really interesting.

335

I would really want to go

back into this data and really

336

thoroughly double check it.

337

I mean, all these curves

look pretty much the same.

338

The, the, the median salary for

onsite is 90, for remote it's 95 and

339

hybrid it's 95 as well, but slightly

a little bit more, uh, skewing right

340

to, to make it a little bit higher.

341

To me, this means, you know, you

guys should really chase hybrid

342

roles because they pay the most.

343

And I think they're actually

not as competitive as remote.

344

They might be a little bit more

competitive than onsite, but still

345

working at least a little bit from

home is awesome, and I think everyone

346

should have the chance to do it.

347

So personally, if I was advising you,

I'd say go for these hybrid roles, but

348

for the most part, it doesn't look like

it affects your salary all that much.

349

So I guess go for whatever

ones you think you can land.

350

Next, I wanna show you how

location makes a difference.

351

As we talked about earlier, you'd expect

if you work in more of a cheap state

352

to get paid a little bit less versus a

more expensive state like California,

353

New York, to get paid a little bit more.

354

So it looks like at the bottom we

have Arizona at 77,000 South Carolina

355

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

no, as the fourth lowest paying place

356

to be a date analyst at $80,000.

357

Followed by Pennsylvania, $80,000.

358

Now I will say the sample size

for these is extremely low.

359

Like for instance, South Carolina,

we only have nine jobs, so it's not

360

necessarily statistically significant,

but just kind of fun to look at.

361

And as we continue to post more jobs,

we'll this data will be updated Next.

362

We have California at a hundred thousand,

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

363

That is shocking.

364

And obviously a small

sample size of only five.

365

Virginia at 131 in Nova Scotia.

366

Small sample size, but

$127,000 as the median.

367

Uh, what I notice here is that California

and Virginia are probably only two

368

that have statistically significant

data to actually say they pay a bunch.

369

California, it's expensive place to live.

370

There's also a bunch of tech companies

like Google and Tesla and all these

371

other companies or whatever, right?

372

And Virginia has a lot of military

and government contractors and it's

373

also an expensive place to live.

374

'cause DC's kind of basically right there.

375

The last thing I wanna break down

for these salaries is what skills

376

are mentioned and what you can

kind of get paid based off of.

377

What skills.

378

You know.

379

The bottom skill is Excel at 88,000,

followed by Power BI at 96,000.

380

Tableau, 99,000 sql a

hundred thousand AWS.

381

102,000 Python 102,000 R,

106,000, Azure 110,000 Snowflake.

382

A whopping 1 21 K,

followed by DBT at 131 K.

383

So what can we learn from this?

384

I think basically what I take away is the

easier a skill is to learn and easier a

385

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

386

The lower the salary expectation is,

for example, we've all learned Excel.

387

We've all used Excel a little bit,

and it's not hard to learn how

388

to analyze data in Excel, and so

that's why you know it's the lowest

389

data tool, lowest paying data tool.

390

Next, there's power behind Tableau.

391

These are your business

intelligence dashboard tools.

392

These aren't super complicated

to get started with.

393

If you can figure out how

to make a PowerPoint slide.

394

You can figure out how to create a

dashboard in Power BI in Tableau, it's,

395

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

396

It's basically click-based.

397

No scripting.

398

Although there is scripting in both of

them, they can get pretty complicated.

399

But to get started, um,

they're pretty simple.

400

Next, you kinda have the sql, Python,

and R group, and these are the

401

languages, um, things that you have to

code and that takes a lot more time to

402

learn and a lot more time to perfect.

403

So that's why they get paid a

little bit more, followed by.

404

Lastly, this cohort of AWS

Azure, snowflake, and DBT.

405

This is more cloud-based.

406

Infrastructure systems type stuff, that's

one hard to learn and two hard to do well.

407

And then three really important to make

sure everything's working correctly.

408

Um, 'cause this is more like critical

infrastructure as opposed to just

409

kind of maybe some analytics.

410

So I still think that Excel Power, bi,

Tableau, SQL, are the easiest data tools.

411

To learn the fastest and

also the most in demand.

412

So this is probably where I'd start.

413

And then once you get more into,

once you've learned those, and then

414

once you've learned those, you can

get into more of the specialty tools

415

like AWS or R or Azure or Snowflake,

and that's what's going to actually

416

help you get paid more in the end.

417

Alright, so I'm hoping all

this salary data made you more

418

informed with all these numbers.

419

And remember that numbers equals

knowledge, and knowledge is

420

power, and power is confidence.

421

So be more confident.

422

You know what you can expect salary wise.

423

Now you know what you need to chase

after, what skills you need to learn,

424

what roles you need to go after.

425

Now be confident and go

out there and get it.

426

I mean, that's exactly why

I built find a data job.com

427

is to help people like you

confidently land data jobs.

428

So make sure you check it

out, links to the description.

429

Thank you for watching or listening,

and I'll see you in the next one.