Lorenzo Caggioni
Google Strategic Cloud Engineer Data And Security
Milan, Lombardy, Italy
Summary
🤩
Rockstar🎓
Top SchoolLorenzo Caggioni is a Google Strategic Cloud Engineer specializing in data and security with 12 years of experience designing and operating large-scale data ingestion, processing, and analytics solutions on Google Cloud. Based in Milan, he combines hands-on backend and DevOps engineering—contributing to prominent GoogleCloudPlatform repos that implement GCS-to-BigQuery pipelines and Apache Beam/Dataflow integrations—with a strategic role helping customers migrate and optimize data platforms. He partners with product and engineering teams to troubleshoot platform issues and drive product excellence, while also leading Google Italy’s Disability and Inclusion efforts as an ongoing 20% role. Trained as a physicist with additional study in digital economy and legal aspects, he brings a quantitative, systems-thinking approach and a pragmatic sense for deployment and governance. Notably, his open-source contributions focus on practical, production-ready data ingestion tooling that bridges engineering and operational needs.
12 years of coding experience
University of MilanDigital economy new business models: Tax&Legal Aspects, Law, Digital economy new business models: Tax&Legal Aspects, Law at SDA Bocconi
Italian, English
Github Skills (15)
data-ingestion10
google-cloud-dataflow10
dataflow-programming10
bigquery10
data-pipelines10
data-pipeline10
dataflow10
python10
google-cloud-datastore10
apache-beam10
data-engineering10
gcp10
google-cloud-platform9
terraformer6
terraform6
Programming languages (8)
Github contributions (5)
GoogleCloudPlatform/cloud-foundation-fabric
May 2020 - Jan 2023
End-to-end modular samples and landing zones toolkit for Terraform on GCP.
Role in this project:
Back-end Developer & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:195 reviews, 457 commits, 183 PRs in 2 years 8 months
Contributions summary:Lorenzo's initial commit introduces Python scripts and a Dataflow pipeline within the context of a GCS-to-BigQuery data ingestion process. The code changes encompass data transformation logic, including parsing CSV rows and adding timestamps, and configurations for a Dataflow pipeline. The user is likely involved in deploying and maintaining code within the GCP environment.
gcpvultrcdrend-to-endvcenter
GoogleCloudPlatform/professional-services
Oct 2018 - Sep 2022
Common solutions and tools developed by Google Cloud's Professional Services team. This repository and its contents are not an officially supported Google product.
Role in this project:Backend Developer
Backend DeveloperContributions:13 reviews, 10 commits, 4 PRs in 3 years 11 months
Contributions summary:Lorenzo primarily contributed to the development of data ingestion pipelines and BigQuery integration, as evidenced by the code changes in `data_ingestion_configurable.py`. They implemented functionality to read data from files, process it, and load it into BigQuery tables. This work leverages the Apache Beam framework for data processing and utilizes Datastore for schema information, improving data integration capabilities.
gcpprofessionalgoogle-cloud-mlgoogledata-stream
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