Daniel Imberman
AI Consultant And Advisor at Imberman AI Consulting
Los Angeles, California, United States
Summary
🤩
Rockstar🎓
Top SchoolDaniel Imberman is an AI consultant and advisor with 11 years of experience building platform-scale systems, data infrastructure, and applied ML for startups and enterprises. He co-led the landmark Apache Airflow 2.0 release, authored production-grade Kubernetes integrations used by Fortune 10 companies, and helped scale Astronomer toward ~$50M ARR as a Staff Engineer. Now operating as a fractional CTO and embedded technical partner, he designs AI-native architectures, optimizes inference costs, and helps small teams move with outsized velocity. Comfortable switching between hands-on coding and high-stakes architectural decisions, he has rebuilt venture-backed AI platforms in days, not months. Based in Los Angeles and an active Airflow PMC member and contributor, he combines open-source stewardship with a practical bias for shipping resilient systems.
11 years of coding experience
10 years of employment as a software developer
Bachelor of Science (B.S.)/Masters of Science(M.S.), Computer Science, Bachelor of Science (B.S.)/Masters of Science(M.S.), Computer Science at UC Santa Barbara
Stackoverflow
628reputation
50kreached
8answers
45questions
Github Skills (22)
apache-airflow10
kubernetes10
docker10
python10
containerization10
dockers10
kubernetes-pods10
devops10
operator10
cicd9
yaml9
logging9
apidoc8
api8
testing8
Programming languages (15)
Github contributions (5)
apache/airflow
Jul 2017 - Feb 2025
Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Role in this project:
Back-end & DevOps Engineer
Back-end & DevOps EngineerContributions:376 reviews, 399 PRs, 387 pushes in 7 years 8 months
Contributions summary:Daniel's commits primarily focused on the development and integration of Kubernetes functionality within the Apache Airflow project. The contributions included implementing and refining a Kubernetes Operator, creating more robust error handling for Kubernetes cluster issues, and adding features such as local log aggregation. Further work involved incorporating pod template files and enabling the ability to specify environment variables, and more. The user also worked on fixing bugs, improving the overall stability of the project, and adding more detailed logging for debugging purposes.
airflowapache-airflowapachepythonscheduler
bloomberg/airflow
Jun 2017 - May 2019
Apache Airflow (Incubating)
Contributions:47 PRs, 374 pushes, 42 branches in 1 year 10 months
airflowapache-airflow
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