Is it worth taking a data bootcamp to boost my career as an MLE?

I'm mostly leaning against it. The pay is lower in Europe, so I imagine $2000 escaping your bank account will hurt quite a lot (and even in the US, this is a large amount for most engineers).

As I talk about in my job searching course, it's best to be fluidly reactive and dynamic when it comes to interviews. My advice is to put out some feelers and apply to those higher-paying MLE/DE jobs. You're bound to run into many rejections, including at the application layer. Figure out what's causing those rejections to identify skill gaps the market is demanding of you. If 50% of rejections are because you don't know Hadoop, then by all means, start a learning journey for Hadoop!

Another angle is to expand your role at your current job, which is by far the best option if it exists. So let's say you have identified that you need Hadoop:

• Is there a way to expand the scope of one of your work projects to use Hadoop?
• Maybe you can build a proof-of-concept that uses Hadoop as a separate project and demo it to your team?
• Does your team have interns that you can leverage to hack around with Hadoop? (Back at PayPal, my interns were working on proof-of-concept stuff and were allowed to use whatever shiny tech framework they wanted)
• The nuclear options is to change teams internally to one that uses Hadoop.

Be creative and explore all possible options!

Also, you can find a community of Data Engineers here in Taro! I recommend attending the Fundamentals of Data Engineering Book Club sessions if possible and networking with folks there. You can reach out to the leader of those sessions Gideon Blinick in the Taro Premium Slack as well - I've talked to him many times and he's very nice and talented.

I have a different take from Alex, as I'm more positive on the BootCamp. Big disclaimer upfront: I haven't taken it, so you should read what I write with a big grain of salt and also take to folks who actually have taken in (and get both the good and the bad from them).

Here are a few things to think about:

One, the cost of the bootcamp is steep at $2K. But compared to other bootcamps that charge north of $10K, it's not so steep. I myself took one of those expensive bootcamps.

Two, a big factor to me is whether or not you're currently working. If not, I think the BootCamp makes more sense. From folks I've spoken to who have done it, the commitment is min 10 hours/week, and can be 20 or 30 depending on how "well" you want to do. Zach gives a "Superb" certification and that means you have to attend every lecture, do all the homework, and have a great project. I imagine that takes a good deal more than 10 hours per week to get. Balancing that on top of a full-time job is doable but difficult. Whereas if you're not currently employed, the structure of the bootcamp can be a good supplement to the applying, networking, and learning you're doing on your own.

Three, the mental-model I have for time spent outside of a job has 6 buckets to it:

1. applying to jobs

2. networking

3. interview prep (e.g. Leetcode)

4. projects

5. courses/learning

Bonus: 6) Open source contributions

I think the BootCamp will help you with all 5 of those. You'll get a capstone project or two, so should hopefully come away with a good resume/portfolio project. You'll learn a ton of DE material that's relevant for Big Tech. I believe Zach has an interview prep module. You'll get to network with and meet other Data Engineers (and other folks taking the BootCamp). And you'll be part of Zach's network so that when recruiters come to him, you'll be a name on his list. And if you do a great job in the BootCamp, you'll be towards the top of that list.

Zach also is marketing the camp towards people who have pre-existing DE knowledge, so the quality of your networking should be higher.

Of course, the decision also depends on your financial situation.

But on the whole, if you're not currently working, I'm leaning towards taking it.

More than the skills that the DataExpert bootcamp will teach, I think it's better to focus on the peer group.

Almost all knowledge is available entirely for free through a combination of YouTube, Wikipedia, and various libraries. The hard part is having structure and accountability to learn what you need. That's where a course or bootcamp could be valuable.

And even then, the skills in data engineering will change rapidly and differ by company. More important is to look who else has been taking the course.

• If it's a bunch of engineers who have worked at the types of companies you're targeting, I'd do it.
• If it's people entirely new to data engineering, I'd be hesitant.

Is there anyway to look at the past/current students?

(Disclaimer: I've spoken to Zach Wilson and know him socially!)