Hugo Louro - Streaming Data Architect

Hugo Louro

Streaming Data Architect

San Francisco Bay Area United States

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Summary

👤

Senior

🎓

Top School

Hugo Louro is a Streaming Data Architect with 11 years of experience designing and shipping distributed, low-latency data platforms across cloud and on-prem environments. He combines deep expertise in Java, stream processing (Flink, Storm, Kafka), and large-scale systems architecture with hands-on contributions to Apache Storm and other prominent open source projects. At RealFevr he drives streaming adoption and prototyping for real-time analytics and prediction pipelines, and previously co-created Schema Registry and Streamline while at Hortonworks. Hugo pairs production-focused engineering—performance tuning, fault-tolerance, and operational support—with research roots in machine learning and signal processing from Stanford and University of Michigan. Known for mentoring teams across time zones, he uniquely bridges R&D and product delivery, advocating robust ingestion patterns (Kafka staging) and containerized deployments.

10 years of coding experience

10 years of employment as a software developer

PhD student MSE in Electrical Engineering. Major: Signal Processing Machine Learning, PhD student MSE in Electrical Engineering. Major: Signal Processing Machine Learning at University of Michigan

BSE MSE in Electrical Engineering and Computer Science (Summa Cum Laude), BSE MSE in Electrical Engineering and Computer Science (Summa Cum Laude) at Instituto Superior Técnico

Portuguese, Spanish, English, French

Github Skills (9)

storm10

distributed-systems10

python10

streaming10

java9

apache9

solr9

kafka9

javas9

Programming languages (2)

JavaHTML

Github contributions (5)

apache/storm

Jun 2015 - Jul 2019

Apache Storm

Role in this project:

Back-end Developer

Contributions:91 commits, 63 PRs, 659 comments in 4 years 1 month

Contributions summary:Hugo contributed significant code to the `apache/storm` repository, focusing on developing and enhancing core functionalities. Their work includes implementing Python scripts to generate reports, and building a Solr connector that included SolrUpdate Bolt, Trident State implementation, fields mappers and integration tests. The user also addressed code review feedback and fixed multi-value fields bugs. This demonstrates a focus on expanding Storm's capabilities and improving existing components.

apachebig-datastormjavaapache-storm

hmcl/kafka-apache

Apr 2016 - Aug 2019

Contributions:6 pushes, 3 branches in 3 years 4 months

kafkajavaapache-kafkaapache

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