Victor Poonai is a Senior Data Engineer with 11 years of experience building cloud-native, scalable data systems for federal clients, currently driving data engineering at DPRA after a multi-year tenure at Booz Allen Hamilton. He specializes in AWS, PostgreSQL, ETL, and designing self-adapting pipelines and ORM frameworks that processed hundreds of gigabytes daily across petabyte-scale document stores. Victor combines hands-on backend and DevOps skills—Docker networking, FastAPI, CI automation and Linux server administration—with applied ML/NLP support and LLM prototyping for regulatory document analysis. An active open-source contributor, he’s improved container tooling (rkt), SQL parsing in Rust, and performance features in high‑performance databases like Dgraph and Badger, reflecting deep systems and distributed-systems expertise. He has a practical track record of automating workflows to save hundreds of staff hours per month and building reusable testing frameworks that cut QA effort by 75%.
11 years of coding experience
7 years of employment as a software developer
Johns Hopkins University
Bachelors Communications: Public Relations, Bachelors Communications: Public Relations at University of Maryland
Contributions:50 commits, 68 PRs, 3 pushes in 4 months
Contributions summary:Victor primarily focused on automating and streamlining the documentation generation process for the project. They created a generalized script for CI, modified the `generate.sh` script to improve automation and handle errors, and integrated a system for publishing documentation to GitHub Pages. The user also introduced improvements for continuous integration, including the addition of the Heroku deployment link to the email and added support for multiple repositories documentation generation.
high-performance graph database for real-time use cases
Role in this project:
Back-end Developer & Database Engineer
Contributions:6 reviews, 134 commits, 156 PRs in 1 year 2 months
Contributions summary:Victor primarily contributed to the core functionality and features of the Dgraph database. Their work included implementing improvements to the bulk loader, adding metrics to the query execution, and implementing a helper method to calculate the size of lists. They also addressed a bug related to group deletion and refactored code related to mutations. Additionally, the user added support for subscription to the graphql layer. Their contributions focused on improving the database's performance, features, and integration with GraphQL.
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