Raghav Sharma is a data engineer based in Seattle with eight years of software engineering experience and an MS in Data Science & Analytics from Georgetown University, where he graduated with a 4.0 GPA. He currently builds data solutions for AWS Marketing and previously delivered production ETL integrations for Firebolt and Prefect at Sigmoid, plus high-impact backend APIs at HSBC that dramatically reduced processing times. His academic work bridged research and engineering—curating 40,000+ SEC 10-K filings and engineering an NLP-backed ETL pipeline that achieved 86% test accuracy for labor-risk forecasting. As a Lead Graduate TA and instructor across database and visualization courses, he translates technical complexity into teachable workflows and reproducible analyses. Comfortable across Python, R, SQL, AWS, Docker, and orchestration tools, he combines hands-on implementation with rigorous evaluation to move models and pipelines into production. Outside typical engineering duties, he’s demonstrated an ability to ship custom DB-API integrations and adapters, reflecting a knack for solving non-obvious interoperability challenges.
8 years of coding experience
4 years of employment as a software developer
Master's degree, Data Science and Analytics, 4.0 / 4.0 GPA, Master's degree, Data Science and Analytics, 4.0 / 4.0 GPA at Georgetown University
B.E, Electronics and Communication, 3.3/4.0, B.E, Electronics and Communication, 3.3/4.0 at Thapar Institute of Engineering and Technology
The open source version of the Amazon EKS user guide. You can submit feedback & requests for changes by submitting issues in this repo or by making proposed changes & submitting a pull request.
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