Nachiket Deo is an Analytical Data Engineer with 10 years of experience applying database and systems research to production analytics and ETL at companies and universities across the US. He combines hands-on data engineering (Databricks, PySpark, AWS Glue, SSIS) with research into column-store materialization strategies and record linkage algorithms, bridging high-performance systems work and practical supply-chain/logistics analytics. Notable projects include building performant ETL pipelines that cut processing time over 30% and contributing parser/AST work to an experimental programming language on GitHub. His background includes static-analysis and compiler toolchain work (clang/clang-tidy) and parallel algorithm implementations, reflecting a strong foundation in algorithms, operating systems, and database internals. Based in the New York metro area with an MS in Computer Science from UConn, he is interested in optimizing storage and query strategies for modern row/column stores and uncovering subtle data-quality patterns across device generations.
10 years of coding experience
6 years of employment as a software developer
Bhagvati vidyalalya
High School ScienceVocational, High School ScienceVocational at Shubham Raje Junior College
Master of Science - MS Computer Science, Master of Science - MS Computer Science at University of Connecticut
Bachelor's degree Information Technology, Bachelor's degree Information Technology at Savitribai Phule Pune University
A blazing fast language for the blazing fast world(WIP)
Role in this project:
Back-end Developer
Contributions:5 commits, 3 PRs in 17 days
Contributions summary:Nachiket primarily contributed to the development of the Peregrine language's parser and Abstract Syntax Tree (AST). They implemented functionality for parsing class definitions, including attributes and methods. These changes involved modifying the parser and AST files to accommodate the new language features and included code modifications to support the integration of these new features. Testing was integrated, reflecting a focus on robust language feature implementation.
Contributions:1 review, 36 PRs, 129 pushes in 2 years 1 month
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