Gordon Hemsley is a Consulting Data Engineer with 17 years of experience designing web and data systems, currently focused on data engineering at LexisNexis Risk Solutions. He combines hands-on scripting and ETL expertise across Perl, Python, Ruby, PHP and SQL with leadership experience progressing from senior engineer to data engineering lead. Gordon contributes to public-interest open data projects—most notably back-end work parsing congressional bill data for the widely used unitedstates/congress repository—demonstrating strong data modeling and XML-to-JSON extraction skills. He holds an M.A. in Linguistics and a B.A. (Summa Cum Laude), bringing a linguist’s attention to detail and efficiency to complex data problems. Comfortable bridging legacy systems and modern tooling, he builds reliable pipelines, QA checks, and automation that improve data quality at scale. He’s motivated by work that measurably improves others’ lives and often applies academic rigor to practical engineering challenges.
17 years of coding experience
13 years of employment as a software developer
Molecular Genetics, Linguistics, Molecular Genetics, Linguistics at University of Vermont
NYS Regents Diploma with Advanced Designation, NYS Regents Diploma with Advanced Designation at Long Beach High School
Master of Arts (M.A.), Linguistics, Master of Arts (M.A.), Linguistics at University of Delaware
Bachelor of Arts (B.A.), Linguistics, Bachelor of Arts (B.A.), Linguistics at Queens College
Public domain data collectors for the work of Congress, including legislation, amendments, and votes.
Role in this project:
Back-end Developer
Contributions:25 commits in 9 months
Contributions summary:Gordon primarily focused on developing scripts to extract and process data related to congressional bills and statutes. Their work involved writing Python scripts using the `lxml` library to parse XML data, likely from a government source, and structuring the extracted information into JSON files. They refactored code to leverage existing codebase and expanded the data extracted to include additional bill information, indicating a strong understanding of the project's data model and the ability to integrate their work effectively.
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