This Hidden Data Engineering Career Paying $500K a year : Nobody is Talking About in 2026

I was making 60 grand a year. We know businesses that get like a thousand applications and then 99% are people they don't even pass on to the interview. I got friends that are in video making over a million dollars a year. Cuz we had a guy go from like 100k to 300k in like [music] 3 months. [music] Okay, welcome back to the Tech Job podcast and today we have a guest I've been trying to get on for many months. His name is Chris Garson and he is the owner and founder of the Data Engineer Academy. Also has some major data engineer roles in big tech companies like Amazon, Lyft, also some startups. He's teaching people how to get amazing careers in the data engineer space, which anyone that follows my content knows those are some pretty high-paying jobs. So, Chris, welcome to the show. Thank you, man. Thank you for having me. I think that's great, but why why don't we start with Why don't you just tell us a little bit about data engineering? What exactly is it at a very simple level? It in a The way The analogy I like to use is it's the foundation for what other tech roles do. So, I like to use the house analogy. The data engineer is the person that comes in and builds the wiring and the pipes in the house and then the data scientist or the AI person might come in and actually build the rooms, build the roofs, make it nice, decorate it. So, I think I always call data engineers the lost cousin or kind of the underappreciated dogs, if you will, because, you know, this room looks amazing, but you can't see the pipes, the electricity, the wirings, right? But it's essential and it's everywhere, right? So, we build the infrastructure for that data to actually be accessed and that way businesses can use that data to do whatever they need to do. Business insights, dashboards, AI models, whatever it is. So, We're cleaning up the dirt, basically. [laughter] Got you. It's an unsexy job, but I think that's why it's been so underrated. Mhm. And why I think it's starting to kind of be brought up to the surface cuz data is just growing exponentially, basically. I Even going back to like Jeff Bezos, somehow he had the foresight to say, "Oh, we're going to collect every piece of data." Back when he was building the first version of Amazon because they were doing ML 20 years ago, right? They were giving book recommendations based off like things that you would search, right? And everybody was like, "Huh, why did I get, you know, recommended this book that I was searching a week ago?" Everybody was Now everybody knows it's, you know, targeted and ML. Back then, it was such a new concept. Even 10 years ago it was a new concept. So, you know, there were people that had that foresight of like, "Hey, we need to save every data even if it costs us, you know, storage costs, right?" So, like a warehouse. But, it's crazy. I think people still undervalue that the data they throw away, right? So, for example, I We have a, you know, a team of reps that have uh Zoom calls and phone calls with prospects that might want to sign up for our academy. I mean, those are 100,000 phone calls that we have. That's data. That's audio that could be converted into some AI model in the future. And I almost, literally last month, I almost deleted it. Then I was like, "Wait, wait, wait, don't delete." I had to tell my team. I was like, "Hold on, that's actual valuable data." So, even that data brings value to my company, right? That I even forgot about. And we're Data Engineer Academy. So, it is uh I think it's underrated. And even as we speak, there's data getting collected and being generated. So, it's it's it's everywhere, basically. What got you personally interested in the data space? Let's talk about your history a little what? It It's funny. I'll give you the the brief kind of overview of me. I went to college I went to school at Boston College, pre-med, wasn't doing well. I was I had to drop out of two classes, and then I actually ended up taking a course on entrepreneurship one summer in North Carolina. I fell in love with that idea, but I didn't think I can be an entrepreneur as a job, so I kept, you know, I became a math major and kept looking for what I'm going to do after college. How am I going to make money? What's my salary? What's my job? And I started doing a lot of online courses, and I taught myself SQL in about 3 days, and then I realized at that point, "Okay, this data stuff is cool, cuz it's like a puzzle to me." I I used to be When I was a kid, I used to just stay up for 12 hours like doing the puzzle until I finished. I would get ADD and just like not move until I finished it. And so, for me, I think that's when I realized, "Okay, data is like a puzzle. It gives you insights, and then you can use it in your business, right? And and actual, you know, impact the business." So, what I ended up doing is in college, I ended up sneaking into machine learning course in the graduate class because they didn't have data courses in the undergrad courses yet. They didn't have them out. That's how like that's how early, you know, quote unquote early ML wasn't taught in colleges 10 years ago. Yeah. That's crazy. So, I had to go sneak into like graduate classes at that point. And then, at that point, I was like, "Okay, let me try to get a data job." And that's when I got a data analyst job at Amazon. Okay. So, what year was that? That was about 2017. Amazon. That's when I moved to Seattle. So, I was there for about 3 years, and I got promoted to a data engineer 2 and 1/2 years in. Here's the crazy thing is, I was doing data engineering work 6 months into my role. But they didn't call it data engineering cuz again, that was so new even 7, 8 years ago. Yeah. What's crazy is, and this is how this company was birthed, that's when I realized if I had someone to tell me what to do, I could have gotten there in 3 months. Not even kidding. And once I realized that, did actually get there in 3 months, but I was stuck in my job for a little over 2 years, kind of doing work I didn't like. Right, being a monkey. We call it sequel monkey. That's kind of the the inside joke, but I think that and kind of fast forward to the creation of this company, I started posting on Reddit, giving free value on Reddit, and then I realized, "Holy everybody has the same problem I had 10 years ago. No one has solved it." Later on I kind of realized, "Okay, this isn't a novel idea. Career, you know, coaching, career advancement has always been a thing. I just didn't realize it was still a thing in the data space." And that's how I created this company. How was the interview process? How was the What was the compensation starting? I mean, what was that like? I was making 60 grand a year, and I was thrilled. I was thrilled. I think my family combined made 60 grand a year. The interview process was about a five-round interview loop. Basically, you do one interview. It might be like a coding round. If you pass that, the next round was like five one-hour rounds. So, this one might be behavioral questions, this one might be Python, this one might be sequel, this one might be another behavioral question. So, you go through six, seven hours of talking to current employees before they give you the offer. So, I went through that. They actually flew me out to Seattle. I did that, got the job. I think I broke down when I got the job. I was like, "Okay, cool." The crazy part about the compensation though, it was like 60, maybe 70 grand. About 20% was in equity, but they do it over 4 years. Amazon specifically was like, "Do you know what a vesting schedule is?" Yeah, so their vesting schedule was like 5% the first year, 15% the second year, and then like 40 40. Most companies do like 25 25 25 25. Amazon was just, you know, predatory, cuz they were like, "We're going to keep you here till the long run. So, um but to their defense, they want you to stay in the long run and they they want you to benefit from the equity increase. Yeah, which is how it Over those four years, that stock went wild. Exactly. So, eventually I was making 130, 150. I left to another company to make 200, maybe a little more. It was a startup. And [clears throat] then I went to uh Lyft, where I was making 350. And again, they gave me a the four-year uh equity vesting. And when the equity pops, I was making almost 450. Now, what companies started to discover is that eventually they would kind of uh try to give you less equity cuz they knew it would raise. So, so for example, I got friends that at Nvidia making over a million dollars per year cuz their stock just went up 10x. But, I think that's one of not to get too ahead of ourselves, but that I think that's one of the interesting things I've seen at Data Engineer Academy after talking to a hundred thousand people, literally on the phone call, not, you know, not subscribers. Most people are working in tech jobs, but not at good tech companies. And so, they don't get equity. So, when they're making like 200, they're stuck at 200. There's no upside. And so, what's crazy is the skill level of these guys and the skill level of the guys at big tech, there's like a 10% difference. 20% difference. But, the pay is sometimes three, four, five x, right? So, to me that was always interesting cuz it was like, oh, these guys are miserable at their job. Right? And so, that again, it goes back to like how I felt at Amazon. I was like, okay, I got to I got to get out. But, again, not to say anything bad right there, cuz to be in that position at 21-year-old at Amazon is a pretty good spot, you know, to put Amazon and Lyft on your resume. I think I think I did okay there. And so, what specific skills Let's just use that B of A guy Yep. who's pretty good. What doesn't he have or he or she have that that Amazon guy has? This person might know SQL. So, right away, they save two to three months. They might need to learn Python, data modeling, cloud, right? AWS, Azure, it doesn't really matter, but those three things they can probably learn in three months, maybe four months. Right? Again, it kind of depends on how many hours they're they're willing to put in or able to put in. And then what we do is help them apply. And so, for them you know, they're looking at potentially getting into the next role well within six months, right? Um much faster depending on how quickly they go. Um now again, that person knows SQL and knows Python, we're not going to waste their time with Python. We'll do mock interviews to make sure they can actually ace it and verify that they actually know. Uh but they don't have to learn it. They don't have to do, you know, three weeks of Python. Like a master's program would, right? It just never made sense to me. You're You know Alex Hormozi? Right, exactly. It's It's his concept of like time to value, right? Get them to their value as quickly as possible. Right? And and that's just what they want, right? They They don't want to do something they already know, you know? Yeah. All right, so it's really it's that cloud, data modeling, and buffing up their Python really is what sets them apart. Yeah, and and interviewing, right? The interviewing actually [clears throat] matters because you can know SQL, but interviewing in SQL at a big tech company might be totally different. Right? Because the thing about the interviews is it's not just whether or not you got the right answer, it's how you came across during the interview. Did you [clears throat] speak out loud? Did you write pseudo code? Did you take a hint when an interviewer gave it to you? Right? And so, that I think is a lot Is that what you have found the biggest difference in again, those high high-paying jobs at the big tech and the startups versus kind of just the general corporate jobs? Yeah, you got you got to be a good communicator. Yeah, because once you get to that level three, where you're making 250 and above, you start doing what's called cross cross collaboration. You just start working with other teams, right? So, if you're an introverted kind of awkward engineer that's like not going to speak up when they see something, you know, companies are like, well, you got to work with the other teams, right? You got to be able to communicate, kind of going back to that Lyft example, we found an insight, let's present it to software engineers and the stakeholders to actually, you know, convince them that, hey, this needs to be prioritized. So, it takes communication, persuasion, all these soft skills that engineers just never thought to work on. And I get it, right? I get it. They're heads down coding most of the day. Um, but once you get to those higher levels, that percentage starts to switch. You know, you start coding less and less and less the higher up you get. Yeah, and and for some of those jobs, you're making director to VP level money. So, there's for an individual contributor, which is crazy. Um, they're expecting that next level. Of course. Of course. And it's it's an underrated [clears throat] skill set and it's one that comes across in the behavioral question part of the interview. And so, at Amazon, Jeff Bezos has these leadership principles that they're basically judging whether you have right frameworks on how to handle certain situations. A good one is, how did you handle situation where you and a coworker disagreed on something? Right? What they're really testing is like, do you have good EQ? Can you tell the story that you're telling right there and then in a way that's understandable, right? And can you be a leader? That's really what they're looking for. Here's what engineers do. They ramble for a whole page or two. It's very scripted. And they list off 20 technologies. Well, when I was working on this company, I worked with XYZ 714 AWS tool. And I'm like the person you're interviewing might not even know what those things mean. Right. You just lost them in the first second. So, it's kind of like dating, you know, if you go up to a girl and you're just like babbling off random stuff, they'll lose interest in you because again, at the end of the day, humans are going to hire other humans. So, you got to be somewhat likable, you know? I'm glad you brought this up because it's actually kind of a sad thing we've seen is most people don't upskill until they've already lost their job. Right? And it's really unfortunate because they're waiting to learn something new at the worst time of their life. And at the most stressful time. And so again, I I wish I can change people's actions. I don't know if I have that much power, right? But it's it's it's one thing where I'm like, just do continuous learning because what worked 10 years ago or 15 years ago might not work next year. With tech moving as quickly as it does, you know, I'm assuming the the pace of learning has to get faster for everybody as well. And so, I do hope people kind of start to realize that and specifically in tech, in this industry, I think people have been kind of spoiled. What I mean by that is 15 years ago, you can get a if you knew if you were a software engineer, you can get a job anywhere, right? Because the demand was so high. What's funny is that all those engineers are kind of going through their first dry market for the first time in 20 years. And so, they're like, "Oh, it's not my fault. It's the market." And I'm like, "When's the last time you learned something new?" "5 years ago." I'm like "Bro, at this point, you got to learn something new every year. Otherwise, you're going to fall behind." And so, it's a lack of ownership that I see a lot of the times from prospects. They're like, "Oh, it's not my fault. It's the market." And look, I'm not saying the market is a 10 out of 10. I also don't think it's a one out of 10, right? It's not as It's not the worst market, it's not the best market. It's somewhere in the middle right now. but everybody's trying to kind of point fingers somewhere else instead of being like, "Well, when's the last time you learned something new?" Like, let's be real with each other, right? So, I kind of take it to an extreme. I like to learn something new every day, but again, there's a there's a middle ground somewhere. If you're thinking about it, if you're on the fence about data engineering, you hear about the big salaries, don't hesitate. Um give his team a call. We'll have all the links for the Data Engineering Academy [music] in the show notes and all the shorts. And uh Chris, glad we finally made this happen, man. Dude, thank you for having me.