So You Want to Work in Data?

Joe's Nerdy Rants #79 - Weekend reads, podcasts, and other stuff

Gordon Wong and I recently had one of our monthly syncs, and as often happens, we dived deep into a question I’m sure he gets as much as I do: "How do I get into data?" It’s a question that lands in my inbox, LinkedIn messages, and conference chats. And more often than not, it’s quickly followed by, "What programming language should I learn?" or "What tech should I master?"

Here’s the thing: while those questions come from a good place and a desire to learn and grow, they're often starting in reverse of how things work in business. Business problems are never solved technology-first. You need to understand the problem you’re solving, then figure out how to solve it. If you want a career in data, especially today, stop asking about tools and start asking about problems.

Gordon puts it well, with his first response usually, "It depends. What do you want to do?" And that, right there, is the core of it. We, as data professionals, aren’t just tool jockeys. Cursor, Windsurf, DuckDB, Snowflake, Databricks, etc. are all cool, but they’re all the same thing - tools. Our value lies in our ability to drive outcomes, to solve problems, and to create impact. The tools are just the means to an end.

I was reminded of something I heard Warren Buffett say years ago at a Berkshire Hathaway shareholder meeting. Someone asked him what to invest in during a potential hyperinflationary environment. His answer wasn’t about gold or specific stocks. It was simple: invest in yourself. Become the best at what you do. Because if your skills are in demand, you’ll find a way to make a living, no matter what the currency is (money, gold, bitcoin, bushels of wheat, kegs of beer, etc). I think that piece of advice is timeless.

So, how do you "invest in yourself" in the data world? Gordon suggests starting by asking, "What do you care about?" What genuinely interests you? Is it sports analytics, understanding climate change, optimizing supply chains, or improving healthcare outcomes? Look at what you read, what you watch, what problems make you think, "Someone should fix that." If you can't find anything you're curious about, then data, which is all about answering questions and driving decisions, might not be the field for you.

Once you have an area or a problem that sparks your interest, then you can start thinking about the "how." Gordon calls it fostering curiosity. Dig into why that problem exists. What data or knowledge are missing? Then, try to build a solution. And I’m not necessarily talking about creating the next OpenAI or Cursor here. Start small. That process of defining a problem, gathering "data," processing it, and arriving at a "solution" will teach you a lot. It also gives you a tangible project, a story to tell.

This approach naturally leads you to the tools. If you’re analyzing vast amounts of data, reach for one of many data processing engines. If you’re building recommendation engines, learn the major approaches in machine learning. The problem dictates the tools, not the other way around.

We also touched on AI, of course. A common question I hear is "Should I even bother getting into data if AI is going to take all the jobs?" My take, and Gordon’s, is that AI is another powerful tool in the arsenal. It can help us solve bigger problems or create efficiencies. But the fundamental need to solve problems isn’t going away. If anything, AI might create new problems that need solving, and hence, more people will be required to work alongside AI to address those problems. It’s anyone’s guess what will happen, but this seems likely for now. So, focus on developing that problem-solving muscle.

And one last piece of advice, especially for those starting out: try to align yourself with revenue-generating parts of a business. Work on projects that directly impact customers or the bottom line. It's often more rewarding and certainly more visible than being siloed in an internal IT cost center. Look for the "pain," as Gordon says; where there's pain, there's usually a budget and a desire for solutions. A business exists because customers pay it to solve a problem.

So should you get into data? If you’re naturally curious and have a knack for solving gnarly problems, go for it and good luck.

Please listen to the audio above or on Spotify (or your podcast platform of choice).

Have a wonderful weekend,

Joe

---

Attending Snowflake and Databricks Summits? I am, and I couldn’t be more excited for the two biggest events of the season.

With all eyes on AI, Monte Carlo is charting the course to ensure that what goes into your LLMs is just as reliable as what comes out. Hot off the press (or pipeline), Monte Carlo is bringing their observability agents - the industry’s first-ever monitoring recommendation and root cause analysis agents - and new support for unstructured data monitoring. 

Here’s what’s launching and why it matters:

• Observability Agents—Accelerate the detection and resolution of data quality issues by deploying AI-powered data quality monitors and identifying root causes with Monte Carlo’s industry-first observability agents.

• Unstructured Data Monitoring—Deliver observability for unstructured data assets, including documents, chat logs, and more, without writing a single line of SQL.

See AI-Ready Data in action at the upcoming summits:

👉 Monte Carlo at Snowflake Summit 

👉 Monte Carlo at Databricks Summit

Hope to see you there!

Thanks to Monte Carlo for sponsoring the newsletter

---

Cool Weekend Reads

Here are some cool articles I read this week. Enjoy!

Why do we dimensional model? - by Johnny Winter

The Copilot Delusion

DuckLake: SQL as a Lakehouse Format – DuckDB

DuckDB flips lakehouse model with bring-your-own compute • The Register

The Open Table Format Revolution: Why Hyperscalers Are Betting on Managed Iceberg

The Who Cares Era | dansinker.com

Evaluation Driven Development for Agentic Systems.

Hidden Cost of Build vs Buy - by Veronika Durgin

---

Upcoming Events

---

If you’re at Snowflake Summit, I’m doing a live event from the conference with Tom Ridings (Matillion) and Srinivasan Swaminatha (TEKSystems) about The Agentic Data Team.

Where: Streamed live

When: June 3rd at 10:30am PT

Register here.

Thanks to Matillion for hosting this event.

---

Calling all Databricks Data + AI Summit attendees! Do you want a no-BS evening event where you can grab a drink and unwind from a day where you’re probably tired of hearing pitch after pitch?

Blue Orange Digital is hosting a no BS event on Tuesday, June 10th at 6 pm PT. You’re all invited.

Join us a casual and unfiltered evening, which an extremely rare treat in today’s hype-filled, jargon-heavy landscape.

Here’s what you can look forward to (other than delicious drinks):
- Hot takes from me on where data engineering actually is in 2025.
- A chance to connect with leaders building AI-native data stacks.
- Chill, small room vibes after a packed Summit day...not just another vendor keynote.

If you’re working in data, building with AI, or leading teams through both, this is the event for you.

🔗 RSVP here:
https://lnkd.in/e_iF3FPx

Thanks to BlueOrange Digital for sponsoring this event.

---

Utah Data Engineering Meetup, June 18. Register here

Iceland - Global Data Summit, June 23-24. Register here

Australia (Sydney, Melbourne) - Data Eng Bytes, July 24-30. Register here

UK - Big Data London, September 24-25. Register here

Helsinki Data Week - October TBA

More to be announced soon…

Podcasts

Freestyle Fridays - So You Want to Work in Data? w/ Gordon Wong (Spotify)

Hamilton Ulmer - Instant SQL with DuckDB/MotherDuck - Practical Data Lunch and Learn (Spotify)

Gaëlle Seret - Change Management in Large Organizations (Spotify)

Freestyle Fridays - AI Denialism is Holding Back the Data Industry (Spotify)

Ryan Russon - Practical ML Engineering (Spotify)

Freestyle Fridays - What Does AI Do to The Craft of Dev and Engineering? (Spotify)

Laura McDonald - Navigating the Complex World of Enterprise Sales (Spotify)

Freestyle Friday - Navigating Data Strategy in the Age of AI w/ Dia Adams & Gordon Wong (Spotify)

Michael Drogalis - Building a Company in Public (Spotify)

John Giles - The Data Elephant in the Board Room, Data Modeling, and More (Spotify)

Zhamak Dehghani - Autonomous Data Products, Data Mesh, and NextData - Q&A (Spotify)

Freestyle Friday - Advice for 2025 Graduates (Spotify)

Jessica Talisman - Libraries, Knowledge, Shitty Tech Jobs, and More (Spotify)

Freestyle Fridays - “I don’t need to learn anything anymore.” (Spotify)

Juan Sequeda & Jesus Barrasa - Unlocking Knowledge with Graphs (Spotify)

Freestyle Fridays - Wartime Data Teams (Spotify)

Tim Berglund - The Art of Developer Relations, Hardware Hacking, and More (Spotify)

There are way more episodes over at the Joe Reis Show, available on Spotify, Apple Podcasts, or wherever you get your podcasts. Also available on YouTube.

Thanks! If you want to support this newsletter

• The Data Engineering Professional Certificate is one of the most popular courses on Coursera! Learn practical data engineering with lots of challenging hands-on examples. Shoutout to the fantastic people at Deeplearning.ai and AWS, who helped make this a reality over the last year. Enroll here.

• Practical Data Modeling. Great discussions about data modeling with data practitioners. This is also where early drafts of my new data modeling book will be published.

• Fundamentals of Data Engineering by Matt Housley and I, available at Amazon, O’Reilly, and wherever you get your books.

• The Data Therapy Session calendar is posted here. It’s an incredible group where you can share your experiences with data - good and bad - in a judgment-free place with other data professionals. If you’re interested in regularly attending, add it to your calendar.

• My other show is The Joe Reis Show (Spotify and wherever you get your podcasts). I interview guests on it, and it’s unscripted, always fun, and free of shilling.

• Would you like me to speak at your event? Please submit a speaking request if you want me to give a workshop or talk at your event.

• If you’d like to sponsor my newsletter or podcast, please get in touch with me.

Could you be sure to leave a lovely review if you like the content?

Thanks!

Joe Reis