Jorge Cespedes is a Senior Data Engineer with over a decade of hands-on experience designing and operating data platforms across AWS, Hadoop, and streaming ecosystems. He builds end-to-end pipelines and infrastructure-as-code (CDK TypeScript), models data with DBT on Redshift, and implements Python-based integrations using Kinesis, Lambda, Step Functions and Airbyte. His background includes leading Big Data efforts at adidas and Telecom Personal Argentina—where he developed Kafka tooling, custom MapReduce utilities and bytecode-based dynamic class builders—demonstrating a blend of systems-level engineering and applied machine learning. As an instructor in Machine Learning and Big Data and a former researcher with publications in pervasive computing, he pairs practical production experience with academic depth. Based in Paraguay, Jorge brings a track record of shipping robust, scalable data solutions and an appetite for solving low-level performance challenges that often go unnoticed.
10 years of coding experience
13 years of employment as a software developer
Bachelor of Science (BS), Software Engineering, Bachelor of Science (BS), Software Engineering at Universidad Catolica Nuestra Señora de la Asuncion
Master of Science (M.Sc.), Pervasive computing, Distributed Artificial Intelligence, Master of Science (M.Sc.), Pervasive computing, Distributed Artificial Intelligence at Universidad Nacional de Asuncion
Short Course, Distributed Artificial Intelligence with Autonomous Agents, Short Course, Distributed Artificial Intelligence with Autonomous Agents at Federal University of Technology - Parana
Scalable, redundant, and distributed object store for Apache Hadoop
Contributions:20 pushes, 1 branch in 2 months
scalableredundantapachebig-dataspark
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