Daniel De Oliveira
Principal Engineer Tech Lead at Williams Racing
Greater Belo Horizonte Brazil
Summary
👤
Senior🎓
Top SchoolDaniel De Oliveira is a Principal Engineer and Tech Lead with 16 years of experience building edge-to-cloud telemetry and real-time data systems for automotive fleets and motorsport, currently architecting live telemetry platforms at Pro Tune. He designs end-to-end solutions from embedded firmware and CAN bus integration to cloud-native pipelines on GCP, delivering sub-100ms ETL for 1000+ telemetry channels and ML-driven analytics using TensorFlow. His background blends mechanical engineering and data science, enabling protocol-level optimizations that squeeze latency and bandwidth out of constrained hardware. Previously he led sim-racing and performance engineering projects at Williams Racing and has run engineering teams and consulting efforts for autosport clients. Based in Greater Belo Horizonte, he pairs hands-on firmware and PCB work (C/C++) with backend and frontend delivery (Python/FastAPI, Flutter), showing rare fluency across hardware, cloud, and ML stacks.
16 years of coding experience
4 years of employment as a software developer
Business in Society Online, Business in Society Online at University of VirginiaCompetitive Strategy Online, Competitive Strategy Online at Ludwig-Maximilians-Universität MünchenEngineer's degree Mechanical Engineering, Engineer's degree Mechanical Engineering at PUC MinasEstácio de Sá University
English, Spanish, German, Russian, Portuguese
Github Skills (75)
apache-spark10
python10
launcher10
r10
big-data10
sql10
sbt-plugin10
spark10
java10
scala10
jdbc10
scala-native9
database9
parser9
http9
Programming languages (7)
Github contributions (5)
music-of-the-ainur/mongodb.almaren
May 2020 - Jan 2026
MongoDB connector for Almaren Framework
Contributions:3 reviews, 3 PRs, 20 pushes in 5 years 9 months
mongodb
music-of-the-ainur/almaren-framework
Sep 2019 - Oct 2022
The Almaren Framework provides a simplified consistent minimalistic layer over Apache Spark. While still allowing you to take advantage of native Apache Spark features. You can still combine it with standard Spark code.
Contributions:8 releases, 52 reviews, 443 commits in 3 years 1 month
apache-sparkspark
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