Latest / The Career Change Podcast with Fexingo: Switching Fields, Bootcamps, and Reskilling / Bootcamp Grads in Data Engineering
Transcript
- Lucas: So there's this stat that keeps floating around from a few years back, but it still holds up: something like eighty percent of data engineering job postings don't require a computer science degree. And that's not because the work is easy, it's because the skills are so practical and tool-driven that a bootcamp can genuinely get you most of the way there. Luna: That's a big deal, because a lot of people hear 'data engineering' and they picture a wall of complex code. But from what I've seen, it's more about knowing how to move data from point A to point B reliably. Lucas: Exactly. And that's the angle I want to take today, because we've covered a bunch of other roles on this show, but not data engineering. And it's a role that's quietly exploded over the last few years. Every company that's doing any kind of analytics or machine learning needs someone to build and maintain the pipelines that feed those systems. Luna: And it's a role that's been hard to fill. I remember seeing some reports about it being one of the most in-demand tech jobs, but not enough people are training for it. Lucas: Right. So let's get specific. I've been talking to a woman named Priya, who was a middle school math teacher for seven years, did a six-month data engineering bootcamp in 2025, and now works at a mid-sized logistics company building their data pipelines. Her story is a really good template for how this transition actually works. Luna: What made her choose data engineering over, say, general software engineering or data science? Lucas: She told me she liked the idea of building things that had a clear, tangible impact. A dashboard that helps the operations team see where shipments are delayed, that's something she could point to at the end of the week. And she also felt that the math she'd been teaching, even at the middle school level, gave her a comfort with logic and problem-solving that a lot of the pipeline work requires. Luna: That makes sense. And I've heard that SQL is the bread and butter of data engineering, which is a pretty accessible language to pick up. Lucas: It absolutely is. In her bootcamp, the first four weeks were almost entirely SQL. And I think that's a smart way to structure it, because SQL is the foundation. Every pipeline you build is going to involve querying a database, transforming the data, and loading it somewhere else. If you can write a solid complex SQL query, you're already ahead of a lot of applicants. Luna: And after SQL, what's the next big skill? Python? Lucas: Yeah, Python is huge, but not in the way people expect. You're not writing algorithms from scratch. You're using libraries like Pandas and PySpark to manipulate data at scale. And then you have the cloud piece, which is where a lot of the actual jobs live. AWS, GCP, Azure — you need to know your way around at least one of them, particularly their data services like S3, BigQuery, or Azure Data Factory. Luna: So it's a very tool-heavy role. That's actually good news for bootcamp grads, because tools can be learned in a structured program. Lucas: Exactly. And that's been the thing I keep coming back to. Priya said she felt like she was learning a trade, not a theory. Every week she was building something, whether it was a simple ETL pipeline or a more complex streaming process. And by the end of the six months, she had a portfolio of maybe eight or nine projects that she could walk an interviewer through. Luna: That portfolio piece is so important. What kind of projects did she build? Lucas: She started with a simple one: pulling data from a public API, like weather data, cleaning it, and loading it into a database. Then she moved on to something more realistic, like building a pipeline that ingests CSV files from a mock e-commerce site, transforms them to calculate daily revenue, and loads the result into a reporting table. Her capstone project was a real-time streaming pipeline that ingests Twitter data and does sentiment analysis. That one got her a lot of attention in interviews. Luna: It's smart to have projects that show a progression from basic to more advanced. That tells an interviewer you can grow. Lucas: And it also gives you something to talk about when they ask the dreaded 'tell me about a technical challenge you faced' question. Priya told me she used the Twitter project to talk about how she handled the rate limits on the API, which required her to build a queueing system. That's a real engineering problem, and she could speak to it with confidence. Luna: So let's talk about the job search itself. What's the interview process like for a data engineering role, especially for a bootcamp grad? Lucas: It's a mix, but the good news is that the bar for algorithmic coding is usually lower than for a general software engineer. You're more likely to get a take-home assignment that simulates building a small pipeline, or a SQL exercise where you're given a schema and asked to write queries that answer business questions. Priya said she had two of those, both of which felt very doable because she'd practiced the exact same style in her bootcamp. Luna: That's reassuring. And what about the salary side? I know data engineers are paid well, but what can a bootcamp grad realistically expect in 2026? Lucas: Based on what Priya and a few other grads I've talked to have shared, entry-level data engineering roles are landing between ninety and one hundred twenty thousand base salary in most major metros. That's a solid number, and it's partly because the demand is so high and the supply of qualified people is still limited. Luna: That's a strong starting point. And it's interesting because the barrier to entry is arguably lower than for a machine learning engineer, where you need a lot more math background. Lucas: Right. Machine learning engineering is a different beast. Data engineering is more about solid logic, attention to detail, and knowing your tools. And those are things a bootcamp can genuinely train you for. I've seen statistics that say something like forty percent of data engineers don't have a computer science degree, which is higher than in other software roles. Luna: That's a stat worth repeating. And it's not just about getting the job, it's about the long-term career path too. Data engineering can lead to a lot of different directions, like analytics engineering or data architecture. Lucas: Absolutely. Priya's already thinking about that. She's been in the role for about a year now, and she's starting to take on more responsibility for designing the overall data model, not just writing the pipelines. She told me that one of the reasons she chose data engineering is that she didn't want to be pigeonholed, and she's already seeing the path open up. Luna: And I think there's something really appealing about the fact that you can see your work in action. When the sales team gets their weekly report on time, they know it's because of your pipeline. That kind of immediate feedback is motivating. Lucas: Yeah, and this is actually something that came up in a conversation we had with a listener the other week, about what keeps people engaged in their work. And it's a good reminder that this kind of practical impact is a big part of why someone would want to make the switch. And on that note, I want to be honest about something for a second. Luna: Go for it. Lucas: This show — all three hundred episodes across the network — runs without a single ad. That's not an accident. It's because a small group of listeners chip in a few bucks a month through buy me a coffee dot com slash fexingo. That's it. That's what keeps these conversations going. So if you've gotten something out of an episode, whether it's a new idea or just the feeling that a career change isn't crazy, that's a way to keep it ad-free. Luna: Yeah, and it's not about perks or anything, it's just about sustaining something that's useful. And we're grateful for everyone who's already doing that. Lucas: Anyway, back to data engineering. One thing I want to hammer home is that the field is not just about the big tech companies. There are tons of data engineering roles at traditional companies — banks, retailers, healthcare systems, logistics — that are less competitive than the FAANGs and still pay well. Priya's at a logistics company, and she's never had a problem with interview competition. Luna: That's a great point. A lot of people overlook those industries, but they're the ones that are hungry for data talent. Lucas: And they often have better work-life balance, too. So if you're coming from a totally different field, like teaching or retail, those industries might feel more familiar and less intimidating than a tech startup. Luna: What's one thing you'd tell a listener who's considering this path but is worried about the math or the coding? Lucas: I'd say focus on the problem-solving, not the math. You don't need calculus for this job. You need to be comfortable with logic, and you need to be persistent when things don't work the first time. Priya told me that her teaching background gave her a huge advantage because she's used to breaking down complex concepts into simpler steps. That's exactly what you do when you're debugging a pipeline. Luna: That's a great reframe. It's not about being a genius, it's about being methodical. Lucas: Exactly. And the last thing I'll say is that if you're going to do a bootcamp, make sure you talk to alumni who are actually working in the field. Ask them what they wish they'd known before they started. Priya said she wishes she'd spent more time on cloud infrastructure before she started, because that was the steepest learning curve for her. But she also said that once you're in the job, you learn fast, because you're using these tools every day. Luna: That's a good note to end on. Data engineering feels like one of those hidden gems for bootcamp grads, and it's nice to have a clear picture of it now. Lucas: Yeah. And if you're curious about whether it might be a fit for you, start with a SQL tutorial this weekend. See if you enjoy writing queries. If you do, that's a strong signal.