Brian Soto
Data Engineer
San Francisco, California, United States
Summary
👤
Senior🎓
Top SchoolBrian Soto is a data engineer with a decade of experience helping startups and small businesses cut data processing and web app costs while retaining modern, scalable capabilities. Based in San Francisco, he blends cloud-native Azure data engineering, Spark/PySpark, and full-stack React/Python development to move legacy ETL into efficient PaaS solutions. His enterprise background includes building secure CI/CD pipelines and batch processing at Microsoft and automating ADLS token refresh and tabular migrations for Fortune‑100 clients at Tata. An active open-source contributor, he has improved core scientific Python projects like NumPy and Astropy—working on einsum performance, dtype support, and precision-related functions—showing attention to both performance and usability. He partners with clients as an independent and founder-level consultant, favoring pragmatic, cost-aware architectures that scale.
11 years of coding experience
6 years of employment as a software developer
Bachelor's of Science Computer Science, Bachelor's of Science Computer Science at Washington State University
Github Skills (9)
astropy10
python10
astronomy10
documentation10
numpy10
testing8
changelist7
changelog7
change-tracking7
Programming languages (6)
Github contributions (5)
astropy/astropy
May 2020 - Nov 2020
Astronomy and astrophysics core library
Role in this project:
Data Scientist & Software Engineer
Data Scientist & Software EngineerContributions:8 reviews, 9 commits, 5 PRs in 6 months
Contributions summary:Brian made several contributions to the `astropy/astropy` repository, primarily focused on bug fixes, code styling, and updating the changelog. They addressed issues related to the `funcs.poisson_conf_interval` function, modifying the codebase to handle different input types. The user also removed hardcoded filepaths from tests and updated the spinner API. This work demonstrates a combination of software engineering skills with an emphasis on fixing and maintaining the library's functionality.
core-librarypythonastronomyscienceastrophysics
numpy/numpy
Jun 2020 - Jan 2021
The fundamental package for scientific computing with Python.
Role in this project:Back-end Developer
Back-end DeveloperContributions:9 reviews, 3 commits, 9 PRs in 6 months
Contributions summary:Brian primarily contributed to documentation improvements and bug fixes within the NumPy library. They addressed docstring issues and added clarifications regarding function behavior, specifically for `allclose` and `isclose`. Additionally, the user contributed to code related to the `einsum` function, adding object dtype functionality and implementing performance improvements. These contributions suggest a focus on improving the library's functionality and usability.
pythonscientific-computingnumpy
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