Rafa(el) Farías

Mechanical engineering at Berkeley gave me my first language for complexity: constraints, feedback loops, trade-offs, and the quiet discipline of making the math match the world.

That training still shows up in how I approach software. I am happiest when the system is understandable, measurable, and sturdy enough to survive real use.

FP&A was the detour that made the rest of the story make sense. Forecasts, operating models, executive questions, and messy source data taught me that technical correctness is only half the job.

The other half is trust: can people understand the answer, defend it, and make a decision before the window closes?

As a solutions architect, I learned to translate ambiguity into something that could be built. Customers rarely arrive with clean requirements. They arrive with pressure, politics, partial context, and a deadline.

That period shaped how I lead technical work now: listen carefully, reduce the problem to its load-bearing pieces, then build the simplest system that can hold.

Eventually the work moved deeper into the platform: pipelines, models, warehouse patterns, lakehouse decisions, orchestration, and the developer workflows around all of it.

The through-line stayed the same. Make the complicated thing legible. Make the important thing reliable. Make the next engineer faster than the last one.

Today I work at the intersection of data platform engineering, technical leadership, and AI-assisted development. The work is still about systems, but the system now includes the humans operating it.

I care about high-quality data, calm developer experience, and tools that make a team more capable without making the architecture more theatrical than it needs to be.