Having worked in data and analytics for over 10 years, and having built two data practices from scratch, I feel like I have a decent read on what makes someone successful in this field. Earlier in my career, most people pivoting into data came from other industries, ie. people looking to shift their careers and pivot into data. However, over the past four or five years, that momentum has really picked up, and many of my hires have come directly out of college.
What’s been interesting is the skillset gap. Experienced hires tend to be more comfortable with SQL, whereas new graduates usually show up with Python experience. That shift has been fun to watch — and worth noting, because as a data professional, you absolutely need to learn both.
Regardless of background, there are a few core traits that I believe make someone successful in this field. Here are five things I’ve learned throughout my career that I would suggest to anyone just starting out:
1. Get the fundamentals right first
Focus on SQL for querying data and Python for analysis. You don’t need to know every tool out there - a solid foundation will take you further than a long list of certifications. What surprised me when I started managing teams was how rarely schools teach data modeling with any rigor. The Kimball dimensional model, hub-and-spoke architectures, semantic layers — these come up constantly in practice and almost never in coursework.
2. Build things that solve actual problems
It’s easy to chase the latest framework or certification. Real data work is about solving problems. Build small projects that mimic real scenarios: analyzing customer trends, cleaning messy datasets, forecasting outcomes. Being able to frame a problem, solve it, and explain what it was worth, ie. in time saved or revenue generated - will do more for your career than technical keywords on a resume.
3. Learn to explain what you found
Technical skills are only part of the job. If you can’t explain your findings to someone who doesn’t work in data, the work doesn’t land. Think of it as translation: your job is to take complex findings and make them useful for whoever’s in the room. Explaining how dbt transforms data into a gold business layer might make sense to an engineer. It’ll lose an executive immediately. Know your audience. (See point 2.)
4. Question the data, not just the analysis
Running the analysis isn’t enough. You have to think critically about the data itself; where it came from, what might be missing, whether there are biases baked into how it was collected. The people I’ve seen succeed fastest are the ones who catch data quality problems before they become someone else’s bad decision.
5. Be okay with not knowing things
Whether you’re just starting out or switching careers, there will be gaps. That’s fine. Data moves fast, and nobody knows everything. The ones who do well long-term are the ones who stay curious and don’t pretend to have answers they don’t have.
Breaking into a career in data isn’t about knowing everything from day one; it’s about building strong habits, staying curious, and focusing on delivering real value. If you keep those principles in mind, the technical skills will follow, and the opportunities will too.