Shiva Deviah is a Senior Software Engineer in Mountain View with 11 years of experience building scalable backend and full‑stack systems, currently at Google after progressing through roles on Google Consumer Shopping. He blends strong academic foundations—an MS in Data Science from USC and a top‑ranked BE in Computer Science—with hands‑on engineering across Java, JavaScript/TypeScript and large‑scale query systems. Notable open‑source work includes performance and memory optimizations to Apache AsterixDB’s hybrid hash join, addressing spill handling and out‑of‑memory risks in real-world big‑data joins. Shiva’s background spans machine learning research, applied search and ads systems, and developer education, reflecting an ability to move from low‑level algorithmic improvements to product feature delivery. Known for tackling tricky performance bottlenecks, he brings both research rigor and production discipline to complex data systems.
11 years of coding experience
5 years of employment as a software developer
Bachelor of Engineering - BE, Computer Science, 9.84, Bachelor of Engineering - BE, Computer Science, 9.84 at PES University
Master of Science - MS, Data Science, 3.8, Master of Science - MS, Data Science, 3.8 at University of Southern California
High School, Indian School Certificate (ISC), 97.25%, High School, Indian School Certificate (ISC), 97.25% at Bishop Cotton Boys' School
Contributions:7 commits, 2 PRs in 3 years 9 months
Contributions summary:Shiva primarily focused on enhancing the Apache AsterixDB's backend functionality related to query optimization and hybrid hash join implementation. Their contributions involved improving the logical plan display, optimizing join operations, and handling large objects within the join process to prevent memory issues. They addressed specific performance bottlenecks and memory constraints related to the hybrid hash join, including managing spilled partitions. The user made adjustments to the code to optimize the usage of memory resources, prevent excessive memory usage, and reduce the risk of out-of-memory errors.
This repository provides a JSON data generator inspired by Wisconsin Benchmark. Advanced features such as variable length strings using various size distributions, nullable/optional attributes, skewed attributes, etc. are added as well.
Contributions:5 releases, 4 PRs, 20 pushes in 8 months
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