Christian Yarros
Senior Cloud Data Engineer
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolChristian Yarros is a Senior Cloud Data Engineer with 10 years of experience designing and operationalizing data and AI solutions on Google Cloud Platform, now based in New York and currently at Google. He bridges production-grade data engineering with applied generative AI, having contributed as an Apache Airflow committer to integrate Vertex AI operators, hooks, and tooling for embeddings, fine-tuning, and multimodal models. His background includes cloud roles at AWS and Oracle, giving him deep familiarity with cloud-native patterns, orchestration, and scalable pipelines. Colleagues rely on him to translate cutting-edge ML APIs into robust workflow primitives that teams can run in production. Notably, his open-source work reflects a focus on making advanced AI capabilities consumable within standard workflow tooling.
10 years of coding experience
9 years of employment as a software developer
Bachelor's degree Computer Science, Bachelor's degree Computer Science at University of OtagoBachelor’s Degree Computer Science, Bachelor’s Degree Computer Science at St. Lawrence University
Spanish, English
Github Skills (15)
vertex-ai10
machine-learning10
data-pipelines10
data-pipeline10
google-cloud10
airflow10
python10
data-engineering10
gcp10
data-integration9
automation9
automations9
orchestration9
mlops9
orchestra9
Programming languages (6)
Github contributions (5)
apache/airflow
Mar 2023 - Dec 2024
Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Role in this project:
Data Engineer
Data EngineerContributions:9 reviews, 19 PRs, 19 comments in 1 year 9 months
Contributions summary:Christian primarily contributed to the integration of Google Cloud's Vertex AI, specifically adding operators, hooks, and functionalities for working with generative AI models, including language and multimodal models. Their work involved creating operators for tasks like text embeddings, supervised fine-tuning, and token counting, as well as implementing context caching and rapid evaluation. This included creating new features and updating existing ones to be in parity with the Vertex AI API, and included testing and documentation updates.
monitorpythonschedulerapacheprogrammatically
aws-samples/aws-cloud-custodian-sample-collection
Aug 2021 - Sep 2021
Contributions:2 commits, 1 push in 25 days
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