This post is a collection of my notes for people interested in changing careers to data engineering. It’s based on my own real-world experiences.

Why listen to me? Well, I walked the walk and did this change myself. I was a project manager outside of data/tech and managed to start over as a junior data engineer. Nowadays, I’m a full-time analytics engineer.

This career change was one of the best decisions of my life, and I hope my notes serve as guidance to others considering the same.

If you have any feedback, comments, or questions just drop me a note (my contact info is on the About me-page).

Note! This post is a work-in-progress. I will add sections to it as I have time to write. Last updated 7.10.2025.

1. Should You Switch Careers To Data Engineering?

I love data engineering, because it’s essential for almost any modern company, without being overly flashy. There are many hotter topics, such as AI and data science, but none of them would work without good data. In data engineering, you get to be the enabler for almost all other departments of your company, but you don’t have to spend all of your time in the spotlight.

And as it looks right now, there will be no shortage of data engineering demand in the foreseeable future.

What makes a good data engineer?

Data engineers come in many shapes and forms (and that’s a good thing!). That said, I personally think you’ll more likely thrive as a data engineer if you enjoy:

  • Math and logic problems
  • Reading/modifying/writing code and documentation
  • Obsessing over tiny details
  • Studying and learning new tech
  • Working in IDEs and databases for long periods at a time
  • Being an unsung hero doing a lot of heavy lifting behind the scenes
  • Communicating data concepts and problems to business stakeholders (not required but will make you stand out!)

Setting expectations and personal finances

If you’re coming into data engineering from a different job or industry, there are a few practical things worth thinking through before you make the jump.

First, you need to mentally accept that during your career change you’ll most likely be switching to a more junior position than your current role (depending on your career so far). This can be surprisingly difficult if you’ve been in a senior role for a long time.

Second, you also need to accept some temporary decrease in salary, or no salary at all for a while (if you’re quitting your job to study).

In the long run, the above will be offset by the fact that data engineering in general is quite well paid (average salary $92k in Germany, ~$100k in the UK, $150k in the US)

Third, If you’re going to study full-time without a steady monthly pay check, make sure you have the financial runway to sustain yourself for about 6-15 months (see next section for time investment).

How long will this journey take?

Once your finances and expectations are in place, the next big question is time. How much should you realistically plan for?

It’s hard to give an exact number, since everyone’s situation is different, but here are some useful benchmarks:

  • Skillio*, Academy and Salt are some prominent data/tech bootcamps in the Nordics: their programs usually take around 1-2 months of pre-studies and 3 months of actual studying → 4-5 months = ~500-600 hours of effort.
  • There are also other data points for similar tracks: Flatiron (15 weeks), General Assembly (12 weeks) = ~500-600 hours of effort.
  • Boot.dev’s blog post: Backend dev -related, but still a good approximation. They approximate going from zero to job-ready in tech should realistically take around 6-12 months = ~500-1000 hours.

From my own experience, I think 500-600 hours is enough to get the minimal level of hireable junior data engineer skills, if you’re starting completely from 0.

Note that this does not include hours for job search or portfolio building! Also, I’m not considering university studies here, since I think it’s a totally different ball game (and includes a lot of other studies as well).

*Full disclosure: I studied here.

Example timelines

Let’s make an example calculation for two scenarios (with the assumption of 600 hours for studying)

  • Not working or working part-time, can devote 40 hrs of learning per week → 600h / 40h = ~15 weeks = 4 months. On top of this job search 2-6 months = 6-10 months total
  • Working full-time, can devote 15 hrs of learning per week → 600h / 15h = ~40 weeks = 9 months. On top of this job search, 2-6 months = 11-15 months total

Sections coming up later:

  1. Self-Study, Bootcamp, University or Job Pivot?
  2. The Essential Skillset for Junior Data Engineers
  3. Landing Your First Data Engineering Role
  4. Succeeding In Your First Data Engineering Role
  5. What’s next?