Sergio Ramos

Analytics Engineer

Empower Pharmacy

Sergio Ramos is a self-taught Analytics Engineer known for building practical data solutions and sharing thoughtful perspectives on analytics, AI, and business.

The Art of Building Reliable Data with Sergio Ramos

A candid conversation with Sergio Ramos on breaking into data, building reliable pipelines, and making AI work for real business problems.

Q & A with Sergio Ramos

Your journey into data wasn't conventional. How did you get started?

I worked all kinds of jobs after high school, from fast food and construction to warehouses. While working at a warehouse, I started using Excel for personal projects. One day, someone on Fiverr wanted to charge me $100 for something simple in Excel, and out of pure pettiness, I decided to learn it myself. Soon after, COVID hit, I lost my job, discovered data analytics through online courses, and never looked back

What helped you understand the value of data beyond just spreadsheets?

A few books completely changed how I thought about data. EOS taught me how businesses use scorecards to measure performance, Outliers showed me how hidden patterns shape outcomes, and How I Built This gave me real business examples where data helped companies make critical decisions. That's when I realized data isn't about reports, it's about making better business decisions.

What's the biggest lesson you've learned about building analytics solutions?

I've learned that understanding the business problem matters far more than building fancy dashboards. My goal is to ask just enough questions to understand what stakeholders actually need without overwhelming them or overengineering the solution. The best analytics solution is usually the one that's simple, useful, and easy to adopt.

What does a reliable data pipeline mean to you?

To me, reliability means understanding every step of the journey—from the source system all the way to the final report. You need to know how people actually use the system, not just how it's documented. I also think data governance is even more important than data quality because if people don't agree on definitions or trust the numbers, the whole system falls apart.

How do you think teams should approach AI today?

I think we should start with business problems, not AI. Sometimes AI is the right answer, and sometimes simple automation does the job better. Personally, I use AI to speed up debugging, learn faster, and remove repetitive work, but I don't see it as a replacement for expertise. It's more like an intern that becomes incredibly useful when you know how to guide it.

If you could give one piece of advice to aspiring data professionals, what would it be?

I'd tell myself that the bar is lower than I thought. I spent a long time believing I wasn't ready, when in reality I was more prepared than I realized. I'd also encourage people to keep learning the fundamentals, read books, develop business context, and use AI to accelerate their learning, not replace it. That's what will make people stand out in the long run.