Rafael Carrasco
Dallas, Texas, United States
Summary
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SeniorRafael Artiles-Carrasco is a Senior Machine Learning Engineer based in Dallas with nine years building data and ML solutions across startups and enterprise teams. He blends strong mathematical training (MS in Computational and Applied Mathematics) and education experience with hands-on data engineering, MLOps, and customer-facing roles at Astronomer and phData, where he progressed from instructor and engineer to lead and senior ML engineer. Rafael is an active open-source contributor to Apache Airflow, improving AWS integrations and pipeline reliability—evidence of his focus on production-grade workflow orchestration. Known as an extroverted "math person," he excels at translating complex models into practical systems and mentoring cross-functional teams. He brings a rare mix of classroom pedagogy, production engineering, and solutions architecture that helps organizations adopt robust, observable ML workflows.
9 years of coding experience
Stackoverflow
186reputation
1kreached
11answers
0questions
Github Skills (18)
apache-airflow10
data-pipelines10
python10
data-engineering10
aws10
aws-glue10
data-pipeline10
automation9
etl9
automations9
documentation8
testing7
airflow6
pipeline6
docker6
Programming languages (10)
Github contributions (5)
apache/airflow
Apr 2022 - Jan 2024
Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Role in this project:
Data Engineer
Data EngineerContributions:8 reviews, 11 PRs, 38 comments in 1 year 9 months
Contributions summary:Rafael contributed to the Apache Airflow project by addressing issues, adding features, and improving documentation related to data pipelines and infrastructure. Their work included fixing exceptions in DAG pickling, enhancing documentation for AWS integration with AWS_DEFAULT_REGION, and adding an example of a taskflow API example for sensors. The user also modified tests related to AWS Glue jobs and added a parameter to pass an ARN to GlueJobOperator, highlighting their focus on data pipeline orchestration and Amazon Web Services integrations.
monitorpythonschedulerapacheprogrammatically
erdos2n/erdos2n
Oct 2020 - Aug 2023
Contributions:31 pushes in 2 years 10 months
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