Summary
Andrew Hoang is a data engineer based in San Francisco with nine years of experience building scalable data platforms, pipelines, and MLOps infrastructure that enable reliable, data-driven decisions. He specializes in ETL/ELT, data modeling, and analytics engineering across Databricks, Snowflake, dbt, BigQuery, Python, and SQL, with strong emphasis on data quality, CI/CD, and Terraform-driven infrastructure-as-code. Andrew has delivered production batch and streaming systems that process tens of GBs daily, integrated LLM-based extraction to improve metadata accuracy, and cut inference and duplication costs through pragmatic engineering. His background spans hands-on research in MLOps and graph databases, teaching and developer advocacy, and practical work to make complex cloud tooling reproducible and cost-aware for teams and students. Notably, he combines applied ML/LLM workflows with rigorous observability—structured logging, schema validation, and uptime-first deployment—to reduce schema errors and accelerate incident recovery. He brings a blend of classroom pedagogy and production delivery, making him effective at translating deep technical systems into accessible, reliable data products.
9 years of coding experience
11 years of employment as a software developer
Master's degree Data Science: Concentration in Data Engineering, Master's degree Data Science: Concentration in Data Engineering at University of San Francisco
Nanodegree Deep Learning Foundation, Nanodegree Deep Learning Foundation at Udacity
Bachelor's degree Computer Science, Bachelor's degree Computer Science at California State University, Fullerton
English