How to Build a Long Lasting Data Career

On professional growth and career resilience

I’ve been working in the field of data since 2006. Yes that dates me a bit, but I’ve had a pretty successful career and enjoyed every minute of it. I’ve been thinking recently about what has made for such a long lasting and enjoyable career and I’ve come up with a few principles that I hope will help you in your career.

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Before we get to that I have a little announcement. Yesterday I published a new version of my book Minimum Viable SQL Patterns. So what's new in this version?

• I reorganized the chapters in the book around concepts and patterns.

• I recreated the database using DuckDB so you no longer need access to BigQuery

• I added a new chapter where the patterns are applied to dbt

• And a lot more smaller changes throughout

Everyone who bought the book has access to the new version in their Gumroad library: https://app.gumroad.com/library. Those who got the Kindle version will see an update in their app or device. If you don’t have it yet and would like to check it out you can go here: https://ergestx.com/sql-patterns-book/

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Principles of Resilient, Long Lasting Careers

Let’s get back to building long lasting careers. The field of data is unique in its ability to create long lasting, resilient careers because even in the age of AI data is not going anywhere.

I define “resilient career” like this:

• Your skills are always in demand

  • You can get a job in any market (especially these days)

  • If you lose your job you can easily get a new one

• You enjoy what you do immensely

Here are some of the principles I’ve noticed and used to build my own resilient career.

Focus on things that don’t change

Jeff Bezos is famous for saying that you should build your business on things that won’t change in the next decade. The same applies to resilient careers.

Would you risk your career learning things that could become obsolete in the future? I wouldn’t. Technological progress will only get faster, tools and techniques will continue to evolve. If all you focus on is tools you’ll have to keep learning new ones just to keep up. But if you focus on evergreen patterns and concepts you’ll continue to thrive. For example in the data field despite technological advances, we’re still moving data from A to B. It used to be megabytes, then gigabytes and now petabytes.

Forget about passion

Passion is a pipe dream. Enjoyment is a better, more natural way of thinking about your work. You can work hard and be completely drained by the end of the day, but if you don’t enjoy it, what’s the point? So the key is to substitute “I’m passionate about…” with “I really enjoy…” and see how that fits.

Run safe-to-fail experiments

Finding work that’s both enjoyable and you’re uniquely good at doing is not a straightforward exercise. You can’t discover it through personality tests, strength finding, etc. The only way is through experimenting. I’ve experimented quite a bit in my career, trying everything from data analysis, data science and even data product management. Most of them were “safe-to-fail” in the context of an existing job, but the data product management required a bit more risk. Once I saw what all the fuss was about I decided it wasn’t for me and went back to data engineering.

Tinker to learn

The best way to learn new skills or technologies is through projects. Ideally these are work projects but that’s not always possible. Carve time out of your day to explore things that pique your interest and find ways to make them valuable to your work. I used this approach to teach myself data science and I still remember everything I learned.

Keep your identity small

This means don’t tie your career success to a certain identity (like data engineer, data analyst, data scientist, etc.) Why? Two reasons. First it will help you if you’re trying to pivot into something new (like I did when I tried product management). Second it will help you explore adjacent areas that you might find interesting. A strong identity (e.g. I’m an academic) could get in the way of learning. I prefer not to label myself as “data x” because I know it could change. I often just say “I work with data.” That’s enough for any networking event or social gatherings.

Surf the opportunity space

One of the key skills I remember learning and using successfully was asking other people at work what projects they were working on or what their day looked like. This allowed me to “surf” the opportunity space and look for ways I could add value or apply what I was learning. You don’t have to be near the water-cooler anymore to see what people are working on these days. With the advent of “building in public” people are posting their projects (and often posting their revenue numbers) on social media. It’s very easy to get inspired and try new things out.

Wrapping Up

The most important idea of this entire post is the first one: “focus on things that don’t change.” So what are those things that don’t change with respect to the field of data? I will be focusing a lot more on these things in the upcoming issues so look forward to more of those coming up.

That’s it for this issue. If you enjoyed it, please leave a comment, like and share.

Until next time.