My Path into Data Engineering: From Brazil to Utah to the Tech World
How a career change from multiple industries became my greatest professional advantage in data engineering.
I Did Not Start in Tech: Why a Nonlinear Path Can Be Your Advantage
I did not start in tech. I moved to Utah from Brazil as a teenager, earned my B.A. at BYU, raised two kids, and built a career across several industries before making a pivot into data engineering in 2018. Seven years later, it is still the best professional decision I have made.
I share my story because I want you to know that a nonlinear path is not a disadvantage. It is actually one of the best things you can bring into tech.
So, What Do I Actually Do?
I am a data engineer. My job is to make sure data gets from point A to point B in a clean, reliable, and usable form. I design, build, and maintain what we call data pipelines.
Think of a pipeline like a system that picks up raw, messy data from different sources, cleans it up, and delivers it somewhere useful, like a dashboard or a database that a business team can actually use.
Here is what my core responsibilities look like day to day:
- Building and maintaining data pipelines
- Cleaning and transforming raw data into something meaningful
- Making sure data is accurate and trustworthy
- Optimizing how data is stored and processed
- Supporting business-critical systems that run 24 hours a day, 7 days a week
The Tools I Work With
When I started, I had to learn a lot of new tools quickly. The good news is that the concepts behind them matter more than the specific tools themselves. Companies all use slightly different tech stacks, but they generally fall into these four areas:
- Languages: I work most in Python and SQL. These are your best starting points.
- Platforms: Cloud and orchestration tools that run and schedule my pipelines automatically.
- Data Warehouses: Where all the clean data lives once my pipelines do their job.
- Reporting Tools: What analysts and business teams use to turn data into decisions.
Learn the fundamentals, and the tools start to feel a lot less overwhelming.
What My Day Actually Looks Like
I get asked this a lot, so here is an honest breakdown:
- Morning: We do a quick stand-up as a team, and then I dig into any pipeline failures or bugs that came in overnight.
- Midday: I work through my ticket queue and collaborate with data analysts who need help answering business questions with data.
- Ongoing: There is usually some mix of process improvements, writing documentation, reviewing a teammate's code, and running tests.
- On-call weeks: A few times a month, I am the person on call, and I want to be honest about what that actually means because I know it can sound scary.
Think of it like owning a house. Things break all the time, but not everything is an emergency. A broken light bulb or a cabinet door that comes loose? You take care of it during normal hours. But a burst water pipe at 2 a.m.? That one you wake up for.
On-call is the same way. Most issues can wait until morning. The true emergencies are rare, but when they happen, you handle them. The responsibility rotates across the team, so it stays fair and manageable.
It is a job where I get to solve real problems every single day. That part never gets old.
Where Can This Career Take You?
The data field is much bigger than one role, and there are a lot of directions you can grow. I started as an analyst and grew into engineering over time. Here is a look at the landscape:
RoleWhat It Focuses OnData Analyst / BI DeveloperReporting and turning data into insightsData EngineerBuilding the data foundationData ArchitectDesigning the overall data structureData Scientist / ML EngineerBuilding predictive modelsAI EngineerA newer and fast-growing spaceData Product Manager / TPMConnecting technical work to business goals
Your path will look different from mine, and that is completely okay.
My Takeaway for You
I came to this country as a teenager and figured it out as I went. I became a mom, went through hard seasons, changed careers in my thirties, and built something I am genuinely proud of.
Data engineering did not require a perfect background. It required curiosity, persistence, and a willingness to keep learning.
If you are a mom thinking about a career in tech, this field has room for you. The work is meaningful, the pay is good, and companies need people who know how to solve hard problems under pressure.
That sounds like a lot of moms I know.
You can do this. I promise.
