Kamal Aboul-hosn is a Principal Software Engineer based in New York with over 15 years designing and operating highly scalable, reliable distributed systems, currently shaping the technical direction for Google Cloud Pub/Sub, Managed Service for Apache Kafka, and Alphabet’s Tapestry moonshot for the electrical grid. He leads large engineering organizations, drives product and reliability strategy, and pairs hands-on backend contributions—such as notable open-source work on google-cloud-go’s pubsub package and the Cloud Pub/Sub Kafka connector—with customer-focused engineering and mentorship. His background spans low-level systems, cloud messaging, and data pipelines (BigQuery and ML integrations), and he’s known for improving performance and stability through pragmatic engineering changes like flow-control and gRPC tuning. Outside engineering he brings creative teamwork as an active drummer, a detail that underscores his collaborative and performance-oriented approach.
10 years of coding experience
19 years of employment as a software developer
Bachelor of Science (B.S.) Computer Science, Bachelor of Science (B.S.) Computer Science at Penn State University
Doctor of Philosophy (PhD) & Masters Computer Science, Doctor of Philosophy (PhD) & Masters Computer Science at Cornell University
This repository contains open-source projects managed by the owners of Google Cloud Pub/Sub.
Role in this project:
Back-end Developer
Contributions:4 releases, 30 reviews, 60 commits in 6 years
Contributions summary:Kamal primarily contributed to the Google Cloud Pub/Sub Kafka connector project. Their work involved adding and modifying code to handle license requirements and Javadoc. Further contributions included fixing size calculations and adding enhancements related to the source connector's schema. The user also made adjustments to the sink connector.
Labs and demos for courses for GCP Training (http://cloud.google.com/training).
Role in this project:
Back-end Developer
Contributions:42 commits, 3 PRs, 20 pushes in 3 years
Contributions summary:Kamal contributed to the `training-data-analyst` repository, specifically by merging a branch named 'datasme'. Within this branch, the user added a notebook that uses scikit-learn to create a model, train on Google Cloud ML Engine, deploy the model and perform predictions. The added code involves BigQuery integration for data extraction, model creation and deployment, and utilization of the Pandas library. This indicates a focus on developing a machine learning model using GCP services.
gcpgoogle-cloud-platformgoogle-cloudtraining
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