Having worked in data and analytics for over 10 years, and having built two different data practices from scratch, I think I can safely say I have a good sense of what makes a data practitioner successful. Earlier in my journey, many of my peers and team members came from industry, 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 to me is the difference in skillsets. Experienced hires tend to be more comfortable with SQL, while new graduates typically come out of the gate with Python experience. That shift has been fascinating to watch, and 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. Build a Strong Technical Foundation
Focus on mastering the essentials first: SQL for querying data, Python for analysis. You don’t need to know every tool on the market. A solid grasp of the fundamentals will serve you far better, and will make it easier to learn more advanced techniques later. Moreover, the concepts of data modeling haven’t fundamentally changed. The Kimball dimensional model, hub-and-spoke architectures, and semantic layers are rarely taught with rigor in school, but they are essential in practice.
2. Focus on Solving Real Problems
It’s tempting to chase after the latest software or certifications, but real-world data work is about solving problems. Build small projects that mimic real scenarios: analyzing customer trends, cleaning messy datasets, or forecasting outcomes. Being able to frame and solve problems and communicate the value created, whether in time or money saved — will stand out much more than technical buzzwords on a resume.
3. Develop Communication Skills
Technical skills are only part of the job. The ability to communicate insights clearly, especially to non-technical audiences, is critical. Think of yourself as a translator: your role is to make complex findings simple, actionable, and relevant for your audience. Tailor your message accordingly. Explaining how data is transformed in dbt and abstracted into a gold business layer might not resonate with executives. Focus instead on what the data means for the business. (See point #2.)
4. Learn to Think Critically
It’s not enough to run analyses, build data pipelines or create AI models, you need to think critically about the data itself. Where did it come from? What might be missing? Are there biases hidden in how it was collected? Employers value people who can question assumptions and spot issues before they become problems.
5. Embrace Being a Beginner
Whether you’re starting your career or transitioning from another field, there will be moments of uncertainty — and that’s normal. Data is a field that constantly evolves. The most successful people are those who stay curious, keep asking questions, and treat learning as a lifelong habit.
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.