Eugene Huang is a Principal Data Engineer and Tech Lead based in New York with 10 years of experience building cloud-native, data-driven systems across fintech and enterprise. He specializes in AWS serverless architectures (Lambda, Step Functions, DynamoDB) and has productionized end-to-end ML pipelines that delivered multimillion-dollar impact, including a $40M annual savings for mortgage processing and an $88M uplift from credit models. Eugene has ETL-ed over 150 TB of IoT and transactional data using Airflow, Dask/Spark and Snowflake, and holds a GCP Data Engineering certification alongside TensorFlow credentials. He combines deep data science chops (XGBoost, PyTorch) with hands-on cloud engineering (CDK, SQS/SNS/EventBridge), enabling fast iteration from feature engineering to production scoring. A four-time hackathon winner and certified blockchain developer, he also publishes tooling and examples on github.com/eugeneh101 that highlight pragmatic solutions for scalable ETL and model deployment. Colleagues know him for translating complex distributed-compute choices into practical, production-ready systems that non-technical teams can operate.
10 years of coding experience
7 years of employment as a software developer
Bachelor of Applied Science (B.A.Sc.) Statistics and Economics, Bachelor of Applied Science (B.A.Sc.) Statistics and Economics at University of California, Berkeley
Ethereum Blockchain Dev Graduate Aug 2017 - Oct 2017, Ethereum Blockchain Dev Graduate Aug 2017 - Oct 2017 at ConsenSys Academ
GCP Certified Data Engineer Feb 2019, GCP Certified Data Engineer Feb 2019 at Google Cloud Platform
Master of Science - MS Computer Science, Master of Science - MS Computer Science at Georgia Institute of Technology
Data Science Fellow Oct 2015 - Jan 2016, Data Science Fellow Oct 2015 - Jan 2016 at Galvanize - San Francisco, SoMa
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