About
I’m a Python developer and data engineer focused on transforming raw data into reliable, high-performance systems.
With experience across ETL design, backend APIs, and data-driven web applications, I create tools that turn complexity into clarity — from Polars pipelines to full-stack solutions.
My work blends technical precision, scalability, and a genuine curiosity for how data can drive meaningful outcomes.
I’m motivated by technical challenges that require architectural clarity, engineering precision, and consistent execution. My main interests revolve around ETL pipelines, data APIs, and high-performance applications — areas where I can combine practical impact with continuous improvement and a disciplined engineering approach.
Highlights
Work values
- Clarity — simple structures and transparent reasoning.
- Reliability — consistent performance at scale.
- Efficiency — optimized results without unnecessary complexity.
Latest insights
I occasionally write about data architecture, engineering workflows, and real-world lessons.
Blog posts are currently available only in Portuguese and English.
- When to Choose Pandas vs Polars?— Polars & Pandas • Jeferson Peter
Pandas and Polars overlap in functionality, but each shines in different contexts. Let’s compare when to use one or the other.
- __slots__ in Classes — Saving Memory— Python • Jeferson Peter
Using `__slots__` in a class prevents the creation of `__dict__` for each instance, saving memory when you create many objects.