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

So I just analyzed 8,554 data analyst jobs to find out exactly what they are paying right now and the results. They even shocked me. And I look at data, job listings for a living. So in this episode, I'll break down 8,000 different salaries. In every way that you possibly can by experience level, by job title, by remote versus in office, by state and by skill. And I'll show you the highest paid job and the lowest paid job and exactly what you need to do to land that $212,000 data analyst role. So let's go ahead and get into it. By the way, if you're new here, my name is Avery Smith. I'm a senior data analyst with 10 years of experience, and now I spend all of my time trying to help people like you land. Data jobs and everything that I'm going to be showing you all the data and all the graphs and all the salaries is going to be coming from Find a data job.com, which is actually a data analyst job board where that you can use to find data analyst jobs. It's actually one that I run and we post dozens of data jobs for free. Online every day that you guys can apply for right now. So if you haven't bookmarked it yet, please go ahead and do so. It's a really useful resource and there'll be a link in the description down below as well. And all the graphs and data I'll be showing you is available as well in our salary report right here. And we'll have a link to it in the show notes down below. And if you're listening only via audio on Spotify or Apple Podcast or something, I'm gonna do my absolute best to narrate everything that I'm showing today. That way you can basically picture the graphs in your head. Okay, so let's get to what actually really shocked me right away, and there's a few things I really wanna highlight. The first one is actually this median salary. The median salary of the 8,553 jobs I looked at was basically 92. Thousand dollars. And I thought that was pretty impressive because if you go online and you check like Indeed or Glassdoor, they're gonna tell you the median salary is like $82,000 or $85,000. And I'm saying it's about seven to $10,000 more, which is like what? 10 plus percent more. It's not insignificant. Now, of course our data sets are very different. We have different amount of jobs, different types of jobs, so on and so forth, so, so it's not something to get too bogged down in, but I think this is a good sign. At least the jobs that I'm posting on find data job.com have a little bit higher salary on general and average. So another reason you guys should be using find data job.com. The other thing that blew me away is the stats right here that out of the 8,553 jobs that I analyzed, only 3,451 of them had anything to do with salaries. Had only any mention of salary or salary data. That's only 40%. Meaning the remaining 60% of data analysts, job listings, don't list the salary. Don't mention the salary at all, which is a huge bummer. Now, some states in the United States require the job poster to actually say what the salary range is, but many don't. And hopefully in the future it'll be a requirement to actually have the salary listed, because otherwise you're wasting job hunter's times, and honestly, you're wasting the hiring manager's times as well. It's just better if we can be transparent and make sure that we know what we're applying for and what you're actually expecting from us. Now, to show you all the nitty gritty on the statistics of what these different salaries are, we're looking at salary, right? That is a quantitative variable. Basically a number. It ranges from zero to, I don't know, $10 million theoretically, right? And we're looking at a quantitative variable. We often wanna look at what's called the distribution of that numeric value Distribution is basically. The shape of the data or the shape of that column, or the shape of that, you know, field or whatever you wanna call it, to show a shape of a data or a distribution. You'd often use what's called a histogram, where you basically create these little bins of the value and you count how many jobs go into those bins, and then you stack a bar basically on how many counts are in there. Now I think histograms are great, but I also think they're a little bit boring. And so what I did was I actually created this raincloud chart right here, which works very similar to a histogram, but I think it looks a little bit cooler and gives us a little bit more of an insight. So let me explain how this graph works. We're actually showing distribution in three unique ways. First, we have basically a histogram up here on the top, but instead of using bars, it's smoothed over. This is called a kernel density estimator or ridge line. I like to call it ridge line. I think it's a cooler name 'cause it kind of looks like a mountain or a hill. And basically wherever you are on the x axis, the higher this is, the more jobs that have a salary that fits around right there. So, for example, you know our median is around $92,000. That would probably be about right here, and that's why you see the peak. So for example, you see that our median is $92,000. It'd be about right here, and that's kind of why you see a big peak around that space. A decent amount of the jobs pay around 92,000 on average, where that mountain is a little bit lower towards the higher ends. This is because there's not many jobs that pay, you know, around $177,000 below this ridge line. You have a dot. Each one of these dots represents a data analyst job listing. The dots are basically placed at their salary and if their multiple jobs have the same salary, they're stacked on top of each other. So this is basically like an upside down histogram, but instead of using bars, we are using dots. This is kind of what's called a B swarm plot, and I really like it 'cause it lets you see, you know, the nitty gritty, you know, all 3000 ish. Of these different dots on the page at once, and then at the bottom we have a classic box in whisker box plot that shows us the median right here, the first quartile or the 25 percentile here, the third quartile or the 75 percentile here. And then you have a whisker on both ends with outliers over here on the right. All three of this. Are showing the exact same thing. Basically the distribution of data analyst jobs, they call it a rain cloud chart because you have this kind of mountain on top with a bunch of dots below it. This is the cloud, and these are the raindrops falling to a flat line on the bottom, which we call Earth. That's why it's called the rain cloud chart. Now, I wanna dive into some of these jobs and. See, you know, why they're paying so high or paying so low. But before we do, if you like charts like this, you like data like this and you want more of it, then you should definitely sign up for my newsletter. It's 100% free. You can go to data career jumpstart.com/newsletter, or there'll be a nice short link down below to sign up. I send cool charts like this. I send data jobs every week and data insights like the salary is $92,000 on average. And if that stuff that you think is going to help you in your career, which is stuff I think is gonna help you in your career, you should definitely sign up. Alright, let's go ahead and dive into some of these lower paying jobs and high higher paying jobs. Let's start with the lower. So for the lower paying jobs, we have this job right here, which I think is quite interesting. If you click on the link, it'll actually open up in a new window. Now this job is actually expired, but we keep the description 'cause we can learn a lot from, this is actually a part-time job that's 15 to $19 per hour and the hourly wage is $14 an hour, which is about $28,000 a year. Fun little fact, if you take your hourly rate and you basically, um, multiply it by. Two, that's the amount of thousands of dollars you make as salary. So 14 times two is about 28, and that's why it's $28,000 a year. Now, if you actually look closely at this, this is only for people who are enrolled at Enzyme College. So not a really good fit for most of you guys watching probably, but I think we can still learn from it. I think the low end of the job is 28,000. I mean, that's super low in the us um, but it is for college students, so it kind of makes sense. It looks like the responsibilities would be to create dashboards using Power bi, Excel, Smartsheets, power Automate Co-Pilot Studio, and API Connectors. So honestly, this is a pretty advanced, um, role, uh, because it, you need to use API connectors and some of these other tools that I'm not even sure a hundred percent what. All these are, and that's why we gave it a mid-level. We actually said this was a mid-level six outta 10. I think that's a little bit high. It probably should be closer to a four, and I would still count it as entry level because it is an internship, but that's pretty interesting. Um, we can go back over here and also take a look at some of these lower paying jobs. A behavior data specialist, a data specialist and a data specialist. And I include data specialist jobs on this website because they're kind of like a step below a data analyst. Oftentimes these roles, let's open up one of these. A lot of these are in, looks like Kentucky, I guess. This one's in Maryland. A lot of the times these roles are pretty simple. So let's click on one of these roles. Maybe this one right here. This is a data specialist for Kentucky Community and Technical College. It looks like this one is still open and the pays about $34,000. That's the salary right there. I like these jobs because they often don't require all that much. Right? So like you need to have an associate's degree, which is fine. Right? Uh, just a little bit of college experience basically. And it looks like you're mostly doing. Tracking and analyzing data, we didn't even capture any skills that it mentions. And so usually the, the barrier to entry for these data specialist roles quite a bit lower than like a data analyst. Obviously they pay less than a data analyst, um, but they can be like a great entry level data analyst type role. Okay, let's go to some of the higher paying jobs. Over here on the right, we can start with this business intelligence engineer role that pays about $204,000 per year. Let's take a look at that. It is remote, which is pretty awesome. Um, we'll talk about more about remote and hybrid and in person here in a second. It is for a company called RTX, that is an aerospace and defense company that provides advanced system and services for commercial, military and government customers. So we're kind of in military government space, and it looks like you would be a technical subject matter expert for the Microsoft data and analytics stack with secondary skills in Databricks and Snowflake. So. Yeah, basically you'd be using Power bi, power Query, dax, Microsoft Power Platform, um, as well as doing some stuff with Snowflake, Databricks, and SQL based systems. And you can see we captured that this requires Python, SQL, power, bi Spark, snowflake and Databricks, or at least those were the things that were mentioned in the job description. Now we rated this a nine outta 10 on the senior level, so it is a pretty senior level role, and you'll notice that some of these roles. That are high paying, are more senior and require a little bit more complicated tools, like of course they're still gonna require Python, SQL, and Power bi, but then Spark, snowflake and Databricks are a little bit harder to get access to, a little bit harder to learn. And so they are kind of reserved for these more high-end, high paying roles. Okay. I found another one I think is interesting. It's this, uh, data analyst role at. Y Ernest and Young, I guess. Right? And it looks like the salary's about $174,000. We actually ranked it only a five out of 10 on seniority. Let's see if we can figure out if we agree or not. Uh, bachelor's degree in some technical fields. That's, that's how I read this, by the way. I know it says like all these specific fields, but I just kind of look at, you know, a bachelor's degree. That's good. Or I guess it has a master's degree with, uh, four years of experience. So this is still kind of mid, it is looking for years of combined experience with these different things. It requires SQL, spark, AWS, Azure, snowflake, and Databricks. So, yeah, we'll, we'll cover this here in a second while how these different salaries depend on these different skills mentioned, but these more tough skills like Azure A w. Cloud-based, infrastructure based, coding based stuff is really probably going to get you kind of these higher paying jobs. Now, let's go ahead and break this down a little bit. Let's go ahead and look at the experience level. So if we look at the experience level, we see something that maybe isn't super surprising that entry level jobs pay the lowest at a median salary of $76,000 per year. Mid-level is next at a median salary of $90,000 per year, and a senior level role. Pays the most at $113,000 per year. Now, that's not really surprising. The more experience you have, the more you'd expect to get paid theoretically, right? However, what I will tell you is that we do have a lot of overlap in the distributions, right? Like for example, there is a decent amount of height entry level over, you know, around the median of the senior level. So there are some entry level data jobs that pay over six figures for sure. Even though the median's only $76,000. And there are some senior roles that even pay below the entry level median. So like for instance, this senior financial analyst role at Amazon somehow apparently only pays $60,000, which is, you know, $16,000 below the median for an entry level data drop. So there's more that goes into how much you get paid than just what your experience level and your entry level, right? So there's more that goes into how you get paid than just your experience level. You can be entry level and be making more than someone. Whose senior level, and that's actually really, you know, counterintuitive to a lot of people. But there's a lot of factors that we're gonna dive into. One of the most important ones is the location. So for example, if we go to some of these higher paying jobs in entry level, yeah. Like for instance, Palo Alto or New York, right? It's expensive to live in New York, it's expensive to live it in California. And so in order to be competitive, they have to raise those rates, even though those are maybe entry level type jobs versus some of these senior levels, like this senior data analyst. Ah, this is in California too, but there's a lot of factors that go into it. Let's go ahead and explore another one. Next one I wanna explore is actually called the job role. And this might be kind of controversial, but I'm, my definition of data analyst is that you are analyzing data to improve an organization. And so I think there's a lot of families or a lot of job titles that fall into the data analyst family. Marketing analysts, financial analysts, business analysts, BI engineer, analytics engineer. Now are some of these roles a little bit different? Sure. But I kind of consider them roughly all to be data analyst. D roles. So the marketing analyst is actually the lowest at an average of 88,000, followed by financial analysts at 93,000. Uh, data analyst, just like strictly data analyst is 95,000. And this one's really surprising. Business analyst was at 99,000 on average, followed by the bi slash analytics engineer at $105,000. I thought the business analyst was pretty interesting because business analyst to me is actually like a little bit easier to get than a data analyst role because a lot of the times you're not needing to be necessarily a data expert. You're more like a business expert who happens. To, you know, data capabilities. So I would really have thought that that would've been a little bit lower on average than a data analyst. But for at least our data set, it's a little bit higher. So I thought that was interesting. 'cause a lot of these, you know, if we go look at one of these roles, I'm gonna randomly click on one of these and this is always an adventure when we're randomly clicking on things. For instance, this just is Excel and Power bi. It's nothing too crazy in terms of what skills you have to have a bachelor's degree, three to five years of experience. We rated it a seven out of 10 on mid, I think that's even a little bit high. But like this job right here is, is nothing super crazy and has a a low salary, but there's also gonna be high paying ones. So it's just interesting. I will say that this bi slash analytic analytics engineer being the higher paying one goes back to what I said earlier, but like the more senior roles, once you're doing more coding, more infrastructure, that often is reflected with a higher paid salary. 'cause that stuff's hard to do and really important to do. Right now. Let's go ahead and look at the work arrangement. Onsite versus remote versus hybrid. And this is something that I think is very interesting. And before I actually get too into it, I wanna just highlight that everyone wants a remote data job, and I get it. I love working remote. I would say 95% of us want remote work, right? However, that's kind of a problem because it definitely is not 95% of the data roles that are remote. In fact, if you come up here to resources and you go to the remote versus. Hybrid versus onsite report, you'll be able to see that only about 15% of data jobs are remote. 23% of them are hybrid and 63% of them, two thirds of them basically are onsite. And this is a really interesting problem because let's just say, I dunno, 80% of us want to be working remotely. Well, that means, uh, a lot of us are going to be, uh, upset because there's only 15% of data jobs. Available that are remote. And that's where I really like hybrid, because hybrid is basically remote in a lot of times. Right? Like, what if I said that you only had to come to the office once a week that's 80% remote. Obviously hybrid's a spectrum, but there is, it's a lot less competitive because you know, people really want the remote jobs and there's actually more hybrid jobs than there are remote jobs. Anyways, back to the Sal. Onsite actually pays the lowest, which I thought was really interesting. I would really want to go back into this data and really thoroughly double check it. I mean, all these curves look pretty much the same. The, the, the median salary for onsite is 90, for remote it's 95 and hybrid it's 95 as well, but slightly a little bit more, uh, skewing right to, to make it a little bit higher. To me, this means, you know, you guys should really chase hybrid roles because they pay the most. And I think they're actually not as competitive as remote. They might be a little bit more competitive than onsite, but still working at least a little bit from home is awesome, and I think everyone should have the chance to do it. So personally, if I was advising you, I'd say go for these hybrid roles, but for the most part, it doesn't look like it affects your salary all that much. So I guess go for whatever ones you think you can land. Next, I wanna show you how location makes a difference. As we talked about earlier, you'd expect if you work in more of a cheap state to get paid a little bit less versus a more expensive state like California, New York, to get paid a little bit more. So it looks like at the bottom we have Arizona at 77,000 South Carolina at 78, Oregon, 78, and my Utah, oh no, as the fourth lowest paying place to be a date analyst at $80,000. Followed by Pennsylvania, $80,000. Now I will say the sample size for these is extremely low. Like for instance, South Carolina, we only have nine jobs, so it's not necessarily statistically significant, but just kind of fun to look at. And as we continue to post more jobs, we'll this data will be updated Next. We have California at a hundred thousand, Indiana at 102,000, Arkansas at 103,000. That is shocking. And obviously a small sample size of only five. Virginia at 131 in Nova Scotia. Small sample size, but $127,000 as the median. Uh, what I notice here is that California and Virginia are probably only two that have statistically significant data to actually say they pay a bunch. California, it's expensive place to live. There's also a bunch of tech companies like Google and Tesla and all these other companies or whatever, right? And Virginia has a lot of military and government contractors and it's also an expensive place to live. 'cause DC's kind of basically right there. The last thing I wanna break down for these salaries is what skills are mentioned and what you can kind of get paid based off of. What skills. You know. The bottom skill is Excel at 88,000, followed by Power BI at 96,000. Tableau, 99,000 sql a hundred thousand AWS. 102,000 Python 102,000 R, 106,000, Azure 110,000 Snowflake. A whopping 1 21 K, followed by DBT at 131 K. So what can we learn from this? I think basically what I take away is the easier a skill is to learn and easier a skill is to, or a tool is to actually use. The lower the salary expectation is, for example, we've all learned Excel. We've all used Excel a little bit, and it's not hard to learn how to analyze data in Excel, and so that's why you know it's the lowest data tool, lowest paying data tool. Next, there's power behind Tableau. These are your business intelligence dashboard tools. These aren't super complicated to get started with. If you can figure out how to make a PowerPoint slide. You can figure out how to create a dashboard in Power BI in Tableau, it's, you know, click, it's drag and drop. It's basically click-based. No scripting. Although there is scripting in both of them, they can get pretty complicated. But to get started, um, they're pretty simple. Next, you kinda have the sql, Python, and R group, and these are the languages, um, things that you have to code and that takes a lot more time to learn and a lot more time to perfect. So that's why they get paid a little bit more, followed by. Lastly, this cohort of AWS Azure, snowflake, and DBT. This is more cloud-based. Infrastructure systems type stuff, that's one hard to learn and two hard to do well. And then three really important to make sure everything's working correctly. Um, 'cause this is more like critical infrastructure as opposed to just kind of maybe some analytics. So I still think that Excel Power, bi, Tableau, SQL, are the easiest data tools. To learn the fastest and also the most in demand. So this is probably where I'd start. And then once you get more into, once you've learned those, and then once you've learned those, you can get into more of the specialty tools like AWS or R or Azure or Snowflake, and that's what's going to actually help you get paid more in the end. Alright, so I'm hoping all this salary data made you more informed with all these numbers. And remember that numbers equals knowledge, and knowledge is power, and power is confidence. So be more confident. You know what you can expect salary wise. Now you know what you need to chase after, what skills you need to learn, what roles you need to go after. Now be confident and go out there and get it. I mean, that's exactly why I built find a data job.com is to help people like you confidently land data jobs. So make sure you check it out, links to the description. Thank you for watching or listening, and I'll see you in the next one.