Felipe Vieira is a Senior Data Engineer based in São Paulo with 10 years of experience building data-driven solutions and machine learning models for finance and communications companies. Self-taught and deeply curious, he blends strong Python and GNU/Linux toolchain expertise (Vim, git) with growing Scala and Spark skills to operationalize ML and big data pipelines at scale. At Telnyx he focuses on powering real-time communications through reliable data engineering, and earlier roles at Santander and Banco PAN centered on anti-fraud and credit modeling with production-grade monitoring. An active open-source contributor, he improved Python tooling and helped modernize DEAP for Python 3 and better test coverage—evidence of his commitment to code quality and developer ergonomics. He pairs rigorous engineering practices with clear data visualization and storytelling to turn large, messy datasets into actionable business recommendations.
10 years of coding experience
5 years of employment as a software developer
Bachelor's degree, Chemical Engineering, Bachelor's degree, Chemical Engineering at Universidade Estadual de Campinas
Exchange Programme (Science Without Borders): 1 year, Chemical Engineering, Exchange Programme (Science Without Borders): 1 year, Chemical Engineering at University of Groningen
Vim python-mode. PyLint, Rope, Pydoc, breakpoints from box.
Role in this project:
Back-end Developer
Contributions:57 commits, 23 PRs, 64 pushes in 3 years 5 months
Contributions summary:Felipe primarily contributed to the `python-mode/python-mode` repository by improving the Vim plugin's folding functionality. Their commits focused on enhancing folding in various Python code structures such as function definitions, docstrings, and code blocks. These changes involved modifications to the plugin's autoload files, specifically `pymode/folding.vim`, and involved regular expression adjustments to better identify and manage code folding.
Contributions:14 commits, 3 PRs, 4 comments in 1 month
Contributions summary:Felipe primarily contributed to improving the codebase by addressing style issues and adapting the code for Python 3 compatibility. They also added and updated tests within the repository's testing framework. These contributions demonstrate an effort to maintain code quality, ensure compatibility with newer Python versions, and improve the testing coverage of the project.
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