Filipe G
Platform Monitoring Specialist
London, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolFilipe G is a platform monitoring specialist and DevOps engineer with eight years’ experience building and operating CI/CD, observability and cloud infrastructure for safety-critical traffic management and enterprise products. He has led DevOps for a railway Traffic Management System, improving build/release automation, monitoring stacks (Elastic Stack, Grafana) and infrastructure-as-code across AWS and Azure. Comfortable bridging development, QA and PM stakeholders, he prioritises work to meet delivery roadmaps while mentoring junior engineers. His background in signalling and control systems gives him a systems-thinking edge for reliability and fault analysis that few cloud-focused engineers possess. He also contributes to cloud data engineering projects and pursues audio engineering and privacy/security interests outside work.
8 years of coding experience
16 years of employment as a software developer
Degree Automation Control and Instrumentation Engineering, Degree Automation Control and Instrumentation Engineering at Polytechnic Institute of Setúbal
Portuguese, English, Spanish
Github Skills (14)
dataflow-programming10
data-pipelines10
dataflow10
data-pipeline10
python10
data-engineering10
gcp10
bigquery9
cloud-infrastructure8
mlops8
infrastructure8
cloud-computing7
machine-learning7
data-analysis6
Programming languages (2)
Github contributions (5)
GoogleCloudPlatform/data-science-on-gcp
Nov 2021 - Feb 2022
Source code accompanying book: Data Science on the Google Cloud Platform, Valliappa Lakshmanan, O'Reilly 2017
Role in this project:
Data Engineer
Data EngineerContributions:1 review, 10 commits, 8 PRs in 3 months
Contributions summary:Filipe primarily focused on modifying and maintaining data pipelines within the Google Cloud Platform, specifically for chapter 11 of the associated book. Their contributions involved updating file paths and job names to reflect chapter-specific data storage and processing. They also made changes to Dataflow job parameters, including the number of workers, and endpoint configurations related to Vertex AI model deployment and prediction. Furthermore, the user pinned a specific version of a dependency to ensure stability in the dataflow pipeline.
gcpdata-analysispythonsciencedata-analytics
graciofilipe/RL_book
Aug 2018 - Nov 2019
Contributions:57 pushes, 2 branches in 1 year 3 months
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