Product Leadership Between Design, Engineering, and Data
I grew up in Rio de Janeiro and moved on my own to San Francisco in my early twenties to continue my studies and build a new life. Since then, my work has moved through advertising technology, wellness, healthcare, and AI. The products changed. Each brought a different system to understand.
Today I live in Truckee, California, and study in UC Berkeley's Master of Information and Data Science (MIDS) program. Design remains the foundation of my practice. Engineering and data science let me follow a product question further, from the reason to build something to the way it works.
Direction becomes real through decisions
Product strategy becomes useful when it changes what a team does next. Which problem deserves attention? What needs to be understood before committing to a solution? Which part can be tested through something small enough to build?
In my work with PhysaFlow, positioning, brand, website, and an interactive capacity calculator needed to tell a connected story. The product question was grounded in physical infrastructure: how to make the opportunity in existing data-center capacity understandable to the people operating it.
I led the work from positioning through delivery, directing two designers and building the website in Next.js, React, and TypeScript. Moving between those responsibilities helped keep the promise, the interaction, and the implementation connected.
Work across the boundaries
Product management, design, and engineering bring different perspectives to the same decision. A priority has consequences for the workflow. An interaction has consequences for implementation. A technical constraint may change what can be promised.
In healthcare product work, those relationships also involve clinical judgment and operational rules. Leading design meant understanding that surrounding work, then giving the team shared patterns it could carry into delivery.
I see product leadership as maintaining that connection: making the direction clear enough to guide decisions, and staying close enough to the work to recognize when the direction needs to change.
Use data to examine the assumptions
MIDS adds analytical depth to that practice. In our research on data-center growth and renewable energy, the interesting work included examining whether the evidence supported a plausible story. In my Garmin visualization project, a familiar dataset made the choices behind aggregation and visual encoding easier to question.
Both experiences carry into product decisions. An attractive explanation still needs evidence. A useful measure still needs context. Knowing what remains uncertain helps a team decide what to investigate next.
A practice shaped by different places
Rio, San Francisco, and Truckee are different parts of my story. Moving between places, disciplines, and industries has made learning an ongoing part of the work. There is always a context to understand before deciding what should change.
That is the thread through my independent practice: product direction, thoughtful interfaces, and the ability to carry an idea into something people can use.
The PhysaFlow capacity calculator series takes one part of that product work into detail, beginning with the meaning of the capacity estimate.