This post includes my top tips and notes for people wanting to change careers into data engineering (or DE for short).
Why should you listen to me? Well, I walked the walk and did this career change myself. I was a project manager (outside of data space) and now I’m a full-time analytics engineer.
This post is work-in-progress. I will add sections to it as I have time to write. Last updated 26.10.2025.
Contents:
- Is Data Engineering For You?
- Choosing a Learning Path
- The Essential Skillset for a Data Engineer
- Landing Your First DE Role
- Succeeding In Your First DE Role
- What’s next?
1. Is Data Engineering For You?
Data engineers come in many forms and personas (and that’s a good thing!). Still, personally I think you’ll likely enjoy DE more if you find the following interesting:
- Solving math and logical problems
- Coding
- Learning new tech/data topics
- Organizing things
- Obsessing over small details
These kind of daily tasks should sound genuinely exciting to you as an aspiring data engineer:
- Writing and modifying lots of SQL/python code
- Working in databases and IDEs for long periods at a time
- Troubleshooting errors and data quality issues
- Writing tests and documentation
- Deploying code to production
- Listening and communicating to business stakeholders
Also, I think data engineers are rarely in the spotlight in the larger scheme of things. So if you’re fine being the unsung hero doing a lot of the heavy lifting behind the scenes and enabling others to do their work better, DE is just the thing for you.
I’ll describe the typical day of a data engineer more in depth in section 5. Succeeding In Your First DE Role.
Pay in data engineering is quite good, although not exceptional. Average pay is $92k in Germany, ~$100k in the UK and $150k in the US. Freelancer rates on average are about $90-100/hour, but can be up to +$200/hour. It doesn’t make you rich overnight, but it’s a solid income.
Data and tech in general are very remote-friendly. Many smaller companies are 100% remote or remote-first (like Breakout Labs). Also, in bigger companies it’s quite common that data teams are fully international, and there can be people from 5-6 countries even in smaller teams.
A WEF report predicts amount of DE jobs will grow 40–50% from 2025 to 2030. Data is needed in everything these days, and the rise of AI will only add to this demand. Even if AI itself will replace some jobs in the future, I think the net effect on DE demand will still be hugely positive.
💬 Personal Note: Before making the decision to go for data engineering as a career (back in 2024), I had been interested in coding, computers and AI for a long time. I also knew that I could handle numbers, math and the logical problem-solving aspect quite well. The possibility to do most of my work remotely or try out freelancing was also inspiring. And as I saw that the demand and pay for DE roles was not going to decrease any time soon, I decided to make the jump!
2. Choosing a Learning Path
Ok, so you’ve decided that data engineering is for you. The next question is: how to actually get into the industry?
I think there are 4 main ways to do this: bootcamp, self-study, university or job-pivoting.
I’ll cover the pros and cons of each.
Bootcamp
This is my favorite option and also how I did my own career change. The main pros are clear: battle-tested curriculums, mentors with real industry experience, and an accelerated learning pace. A full-time bootcamp means 40–50 hours a week of focused study, or about 500–600 hours (3–4 months) to become employable. Adding job applications and interviews (1–2 months), it’s realistic to land your first DE role in around 6 months.
Good bootcamps have industry contacts and often arrange interviews, prep sessions, and other support since their reputation depends on your success.
There are downsides of course: bootcamps are usually tough to get in, they have an intense learning pace and they usually cost something. You might need to pay a fee upfront or share future income with the bootcamp. Also, you need to account for possible lost income during your bootcamp study time.
Still, I my opinion, the pros far outweigh the cons: fast learning, strong mentor and peer support, and a clear path to your first role.
If you’re in the Nordics like me, check out at least the following bootcamps: Skillio*, Academy and Salt. Internationally, Le Wagon and Flatiron School are solid choices (though Flatiron leans more toward Data Science).
* = Full disclosure, I studied here.
Self-study
Self-study works well if you have time, patience, and strong self-discipline. It’s also one of the most cost-efficient routes to study data engineering: self-paced online learning paths typically cost only $20–150/month.
Web is full of learning paths that you can follow, but the real difficulty comes from being able to separate the relevant information. On the flip side, you are fully free to construct your own learning path from any kind of content you like. Also, you’re schedule is totally up to you: study whenever you want, wherever you want.
I’d say it’s reasonable to expect roughly 25% more time and effort than when studying in a bootcamp: about 625–750 hours of study, or 4–5 months full-time and 8–10 months part-time. Add 2–3 months for building a portfolio and job hunting, so the full self study journey typically takes 6–12 months.
The biggest hurdles with self studying are the lack of structure, mentorship, and industry connections. You’ll need to prove your skills on your own and work harder to get that first role. Finding a study group or mentor can make a big difference. The data industry (or tech in general) is not very degree-oriented, so the lack of formal credentials that comes with self-study should not be a big issue, especially so if you already have one degree under your belt.
Despite the effort, the self studying path is absolutely viable, especially if you study while keeping your current job to maintain financial stability.
Some good learning paths that I’ve come across are: Datacamp, Zero to Mastery and DataExpert. Note that you’ll likely need to mix and match resources to cover all essential skills.
University
The traditional path. If you’re young, starting your career from scratch or have the time and resources, a computer science or data-related degree is still a solid way into data engineering. It gives you a deep theoretical foundation and a broad understanding of how data, software, and systems work.
That said, this route takes the longest time: easily 2-5 years depending on whether you’re doing a Bachelor’s, a Master’s or some kind of a shorter program. Also, uni degrees are notoriously slow to evolve: in the real world new technologies pop up almost every month. You’ll likely still need to learn technologies like Databricks, dbt, Snowflake, Azure, Fivetran etc. more deeply on your own, since no uni degree will ever cover all the nuances of these tools.
If you already have a degree in another field, I wouldn’t recommend going the full degree route again. Your time is much better spent on a bootcamp, self-studying or on a shorter specialized program.
Also one thing to note is that there rarely exists a university degree purely specializing in data engineering. Most likely you’ll have to look for a computer science, machine learning or a data science degree and then specialize while studying. I won’t list any degrees here, as there are too many and your needs will anyway vary depending on your location.
Job Pivot
I think job pivoting is a route that isn’t talked about nearly enough. It has a lot of the same pros than bootcamps and self-studying without having to sacrifice your income level or career trajectory.
Job pivoting basically means transitioning into data engineering from within your current company or role. This is perfect if you’re already doing data-related tasks in your current job and want to learn data engineering more in depth. So for example, you could already be doing some BI analyst work or be in a business consultant role exposed to data daily.
I see two main scenarios how this could play out:
- Convince your employer to attend a data bootcamp while they cover the expenses, and pivot your role afterwards
- Get gradually onboarded to DE tasks while self-studying on the job
But here’s the best part: you still most likely get a full salary in either cases. And if you get to attend a bootcamp, this might be fastest route of all that are mentioned in this post: 3-4 months of studying and you’re ready to pivot to your new DE role.
The challenge here is of course that you actually need to get your employer convinced that this is worth doing. So you need to build your business case showing the numbers and benefits. Also, you’ll need a realistic plan to pivot from your current role to your new one without burning out.
Still, if you can make it work, I think job pivoting is one of the smoothest and most financially stable ways to become a data engineer. It’s especially effective if you’re already close to the data pipeline in your current role.
💬 Personal Note: I went through the bootcamp route myself, after realizing that trying to self-study 1-2 hours during evenings while being already tired from my day job was not going to cut it.
For me the ability to focus full-time on studying, structure, feedback and peer support made all the difference. Having mentors to ask “why does my pipeline keep failing?” instead of googling for 2 hours straight kept my motivation high.
In hindsight, going the bootcamp route was one of the best decisions I made. I spent about 6 months of studying in the bootcamp (including pre-studies) and was employed right after to my first DE role.
But that doesn’t mean that it’s necessarily the right route for you. Everyone’s situation is different. Do your own research and base your decision on the available facts.