Rodrigo Parra is an AI Tech Lead and seasoned software engineer with 15 years of experience building data-driven systems and leading remote engineering teams from Miami. He co-founded Codium S.A. and currently leads AI initiatives at WillDom, where he helps scale a proprietary talent management platform that powers distributed hiring across the Americas. Academically trained in Informatics Engineering (Universidad Nacional de Asunción) and pursuing an EIT Data Science master’s at TU Berlin, Rodrigo brings strong foundations in machine learning, big data processing, and cloud architectures. His open-data work includes contributions to CKAN—improving search indexing and test coverage—which reflects a long-standing interest in open source and data portal technologies. Comfortable alternating between hands-on backend development, QA, and technical leadership, he also consults independently on technical projects. He combines entrepreneurial drive with research-oriented training, aiming to bridge ML research and production-scale data engineering (Spark/Flink) in real-world products.
15 years of coding experience
1 year of employment as a software developer
Informatics Engineering, Computer Science, 4.19/5, Informatics Engineering, Computer Science, 4.19/5 at Universidad Nacional de Asunción
Master’s Degree, EIT Data Science - Year 2, Master’s Degree, EIT Data Science - Year 2 at Technische Universität Berlin
Master’s Degree, EIT Data Science - Year 1, Master’s Degree, EIT Data Science - Year 1 at Technische Universiteit Eindhoven
CKAN is an open-source DMS (data management system) for powering data hubs and data portals. CKAN makes it easy to publish, share and use data. It powers catalog.data.gov, open.canada.ca/data, data.humdata.org among many other sites.
Role in this project:
Back-end Developer & QA Engineer
Contributions:6 commits in 2 months
Contributions summary:Rodrigo's contributions primarily focused on improving the CKAN data management system. They addressed documentation issues, fixing typos and removing redundant information in test documentation. Additionally, they added resource extras to the Solr index, enhancing the search capabilities of the system. Furthermore, the user wrote tests to validate the indexing of resource extras, improving the quality and reliability of the search functionality.
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