Saadhvi Umesh
Senior Data Engineer
New York City Metropolitan Area United States
Summary
🤩
Rockstar🎓
Top SchoolSaadhvi Umesh is a Senior Data Engineer based in the New York City area with four years of hands-on experience building data infrastructure and back-end systems. She has progressed from internships at Tesla and The New York Times to production-focused roles at Featureform, Packable, and Uniphore, where she now leads data engineering efforts. Saadhvi contributes to open-source projects—most notably adding Typesense vector-search integration and robust testing to Featureform’s virtual feature store—demonstrating strength in integrating external services and improving search and feature retrieval. Comfortable working across Go and data stacks, she combines engineering rigor with a data-science foundation from UC Berkeley to operationalize ML features and scalable pipelines. Colleagues describe her as a pragmatic problem-solver who favors tested, refactor-friendly solutions that make complex data systems easier to maintain.
4 years of coding experience
4 years of employment as a software developer
Bachelor's degree Data science/Computer Science, Bachelor's degree Data science/Computer Science at University of California, BerkeleyInternational Baccalaureate Diploma Programme, International Baccalaureate Diploma Programme at John A Ferguson Senior High
Github Skills (12)
vector-database10
go10
feature-engineering10
data-engineering9
apidoc9
api9
machine-learning8
data-science8
python7
testing7
feature-store7
mlops6
Programming languages (1)
Github contributions (1)
featureform/featureform
Mar 2022 - Mar 2022
The Virtual Feature Store. Turn your existing data infrastructure into a feature store.
Role in this project:
Back-end Developer & Data Engineer
Back-end Developer & Data EngineerContributions:2 reviews, 23 commits, 8 PRs in 8 days
Contributions summary:Saadhvi focused on integrating Typesense, a vector search engine, into the Featureform project. Their contributions involved creating and modifying Go files related to Typesense functionalities, including schema creation, data upserting, and search implementation. They refactored code, added tests, and updated dependencies, demonstrating a focus on integrating and testing external services for feature storage and retrieval. The user also implemented a search interface and methods, indicating a focus on improving search capabilities within the project's data infrastructure.
pythondata-qualitydata-scienceembeddingsinfrastructure
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