In this video Felicia, a self‑taught data scientist, ranks five tech roles for the next five years based on five criteria: pay, flexibility, difficulty of entry, resilience to AI automation, and how much of a professional’s existing experience can be transferred. She explains why traditional advice to "learn to code and start from zero" is often a bad fit for people with years of work experience. The rankings (from lowest to highest) are: 5️⃣ Data Center Technician – high demand and $70K pay but physically intensive, low skill transfer, and on‑call work; 4️⃣ Cybersecurity – average $130K pay and strong demand, yet entry requires certifications that demand years of security experience, making it hard for most career changers; 3️⃣ AI Engineer – highest ceiling at $145K, but requires advanced math, a graduate degree, and 5‑8 years of preparation, so it is not a pivot‑friendly path; 2️⃣ Technical Project Manager – around $100K, leverages existing management, communication, and coordination skills, is AI‑resilient, but candidates must clearly demonstrate how their past responsibilities translate to the new role; 1️⃣ Data Analyst – average $86‑90K, the only role where many professionals are already performing the core tasks (reporting, tracking metrics, answering data‑driven questions) without the title. By adding a few technical tools, analysts can double their salary within 6‑12 months. Felicia stresses that data analytics is the most accessible, high‑paying pivot for experienced workers, warns against chasing hype, and invites viewers to watch a follow‑up video outlining a concrete roadmap to land a data analyst job.
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If you want a high-paying job in tech, you've probably heard the same advice. Learn to code, get a bunch of new skills, and start over from the bottom. But if you have years of working experience, starting over from zero is the worst thing you can do for your career. With AI taking over the more routine work, some tech careers are becoming harder to break into, while others are actually becoming more valuable. So today I'm going to be ranking the highest paying tech careers over the next 5 years based on pay, flexibility, difficulty of entering, how well they'll survive AI, and how much of your past experience you can actually use to get in. Now, if you're new here, I'm Felicia, and over 6 years ago, I was delivering pizzas and Amazon packages for about $8 an hour before becoming a self-taught data scientist. And over the past 5+ years, I've helped other career changers break into tech without starting over from zero. And if you ever want to work with me to land your first job in tech without starting from zero, click the first link in the description. Now, before we start, I need you to know how I'm ranking these because a job may pay great, have huge demand, and still be the completely wrong choice for If you have plenty of working experience, the question is never, can you eventually get the job? It's really, what will it cost you to get there? Especially when you're working full-time with bills and maybe only have as little as 10 hours a week to study. So let's start with number 5, the most hyped career on this list. You may have been hearing about data center technicians due to the whole AI hype and bubble. The big tech companies are putting close to $700 billion into data centers over the next year alone. And the job pays an average about $70,000. But if you're pivoting into careers and you have a non-technical background, almost none of what you already know carries over because entry here is physical, not academic. You're learning about power and cooling systems, installing and swapping out hardware, running on on-call rotation that covers nights and weekends because this is not a regular 9 to 5 type of job. And the certifications that you would get that would actually move your pay up are more on the electrical and facility type, which you earn on site, on location, not remote on your So the most hyped career is on the list as number 5 because a booming industry does not mean a good job for you. Want infrastructure work? Go get it. You want your existing experience to count though? This is not it. So if a career that's just hard to automate through AI isn't enough on its own, what about another career with a lot more flexibility? Which leads us to number 4, cybersecurity. On paper, it is the best paying career on the list. It pays around $130,000 on average. Companies desperately need it. We're always seeing cyberattacks and hacks all the time, and the risks are not going away. And the value of the work is obvious. But what you're greatly underestimating is the stack of specialized knowledge that sits underneath it. You start with entry-level certifications, which means learning how networks are built, how computer systems run, how all of it gets locked down from zero. And then you find out that the certifications that you actually need requires 5 years of paid security experience before they will even let you sit for the exam. So the credential that gets you is actually gated behind the job that you're trying to get. And I'm not talking theory here. I know fellow military veterans with years of experience behind them who spent years trying to get into cybersecurity and still could not crack it. It's nowhere near as easy to break in as it's being promised. Now, cybersecurity is not a bad career. It's just a bad first move for many of you who want to pivot into tech. But if you spent 5 years working around computer systems or IT systems, it seems like a natural next But if you spent 11 years running healthcare operations, your healthcare knowledge will do nothing for you in your pivot into cybersecurity. And with employers being far more selective than they were during the pandemic, you want to reduce the number of things a hiring manager takes on faith. So this is number 1 on pay, but near the bottom on how fast you can actually get hired, which raises a fair question. If you're accepting a big learning curve anyway, why not aim at the career with the biggest upside? All right, quick pause. Now, if you want to land your first job in data analytics and avoid the lengthy mistakes that I did, click the link in the pinned comment where you can find the exact steps that my students use to land their job in data analytics without going back to school. All right, now let's get back to it. Which brings us to number 3, AI engineering. And the ceiling is the highest of all the roles here. AI engineers make around $145K. So if you're changing careers anyways, why not go for the thing everyone calls the future? Because AI engineering runs on advanced math and statistics. And then computer programming built on top of that. And let me be clear about something that no one selling an AI course is going to say. AI engineering is not an entry-level role. It is not a career pivot-friendly role in any way. Realistically, you're going to be looking at like 5 to 8 years in preparation, plus a graduate degree in math or computer science to get there. That is not a pivot. That is a second career built from the ground up. Yes, you can learn it. The question is how much you have to learn before someone even pays you a dollar. And chasing the highest ceiling is how you can spend years preparing for a job that you could have reached in months in a completely different way. So if you already write software for a living, sure, you can try AI engineer. But if you don't, this is the wrong door to go through. Highest ceiling and the highest barrier to entry is the worst combination for a first move or a career changer in tech. And you do not get paid extra because your career transition was harder than it needed to be. Now, so far, every path has asked you to prove brand new abilities before for your existing experience actually helps you at all. So let's go into the other direction and actually find where being experienced actually becomes the advantage. That leads us to number 2, the technical project manager. And this is where you start recognizing yourself. 3 departments involved, one person waiting on another, deadlines moving, leadership wants an update, and somehow you're the person getting everyone aligned and keeping it moving. Project managers can make around $100,000. And the experience, the communication, the judgment, and knowing what a project everyone swears is fine, is absolutely not fine are exactly the human skills that get more valuable as AI absorbs the admin work underneath it all. So the barrier here is not knowledge, it's actually evidence. Because inside your company, everyone knows what you do, but a new employer doesn't. You might run the day-to-day operations for a medical office and actually have handled software rollouts, vendors, staff training, timelines, executive communication, operational changes across multiple locations. And then your resume say you just manage daily office operations. So while your ability may be there, your evidence is hidden. So if you do go this route, the work is not another credential. It's really just translation. What did you do to lead? What problems did you solve? And what changed because you were there? It's high on AI resilience and also high on experience transfer. It's number 2 and not number 1 because your first opportunity still highly depends on convincing a stranger that your experience transfers. And that's exactly what makes number 1 different because in this career, part of what you already do isn't just transferable, it is literally the entire job. So number 1 is data analysis. It is number 1 because it is the only career on this list where you are literally already doing the job, but you probably don't know it yet. Right now, the average data analyst job runs about $86K or even up to $90K depending on where you live. And I want you to think about the daily tasks that you have at work right now. Does someone come to you asking why numbers drop? Or do you find yourself building reports or working in spreadsheets all day? Do you find yourself comparing this month to last month, tracking staffing, revenue, new collections, managing processes, improving processes, and pulling numbers for leadership before a meeting, or when someone asks you what those numbers actually mean. All of that is Data Analytics 101, whether you have the title or not. And this is where you're probably going to be selling yourself short. And I've seen this with my students over and over again. And in the data community, someone trying to pivot in will tell me they have no analytics experience, but then I ask them what they actually do at work. And then 10 minutes later, I would find maybe like 4 to 5 things that were already analytical. They just never call them analytics. So No one ever gave them credit for it, including themselves. And that is a completely different starting point because if you already work with business numbers every single week, we're not starting from zero in our pivot. We're just translating our experience that you already have and adding some technical skills on top of it. So say you've spent 11 years in healthcare operations. You already understand staffing, collections, providers. You know what happens when a number starts moving the wrong way, and you know which numbers leadership actually cares about. Someone can teach you a new tool in a few months, but no one can teach you the 11 years of judgment and domain experience that you have in your industry. So don't run away from your old or current industry either. Healthcare needs people who can make sense of its numbers, and so do insurance, logistics, government, and supply chain, and pretty much more. Any industry that runs on data needs analysts, which is all of them. And the outcome here is way bigger than the number on the offer letter. It's getting interviews for roles that pay 2x your current salary that you probably never thought you could actually get. It's walking into those interviews with something to show instead of apologizing for your background. And it's doing the work that uses all years of experience instead of erasing them. Now, if there's one thing I hope you learned from this video so far is that you want to be careful chasing trends. And sometimes the best and highest paying move is the boring one. I highly believe that data analytics is the best tech career for career changers because it is the easiest pivot into and the lowest barrier to entry where you can take your existing experience, learn a few tools on top of it, and can double your salary in 6 to 12 months. In the Navy, we were taught that every sailor is a firefighter, but not everyone's title was a firefighter. It is a function that you perform regardless of what your job description says. Your title may have never said analyst, but every single professional is a data analyst. Data analysis is in everything that we do, which makes data analyst the best pivot for a career changer into tech. Just because your title doesn't say analyst does not mean you have not been analyzing data. So if you just realize you've already been doing data analyst job this entire time, the next step is turning that into an actual career move. Now, watch this next video where I'll show you exactly the roadmap to land a data analyst job in as little as 6 months. I'll see you there.
