Summary
Howard Song is a Principal Software Engineer and data practitioner with nine years of experience building production data platforms, predictive models, and data-driven web applications. He combines hands-on data engineering (Snowflake, Airflow, AWS Lambdas) with applied data science skills in R, Python, and SQL to deliver ETL pipelines, automated reporting, and A/B-driven product experiments. At CBRE he recently led development of a data quality monitoring product that integrates FastAPI, React, PostgreSQL and Snowflake with LLM-enhanced workflows, reflecting a blend of modern backend/frontend and generative-AI integration. His background spans building virtual loyalty economies and pricing/simulation models for gaming, plus independent consulting across clinical research, betting, and energy — an unusual mix that signals strong domain adaptability. Based in Las Vegas, he favors pragmatic, automatable solutions and has a long-running habit of continuous study and practice that fuels steady technical breadth.
9 years of coding experience
10 years of employment as a software developer
Rancho High School
Human Developmental and Regenerative Biology, Human Developmental and Regenerative Biology at Harvard University