Daniel Clark
Staff Software Engineer at Google
New York, New York, United States
Summary
👤
Senior🎓
Top SchoolDaniel Clark is a Staff Software Engineer with 12 years of experience building high-throughput, reliable systems for cloud and real-time integrations, now focusing on observability and telemetry at Google using Prometheus and OpenTelemetry. He has scaled systems handling 200k+ requests/sec at LiveRamp and helped run Managed Service for Prometheus on GCP, combining hands-on engineering with product-focused mentorship. His background in signal processing and machine learning—shaped by an MS in Electrical Engineering from Columbia and research work on large neuroimaging pipelines—gives him a strong foundation for data-driven infrastructure and tooling. He contributes to open-source neuroimaging workflows (notably improving AFNI pipelines and S3/MD5 support in nipype), reflecting a habit of making scientific tooling more robust and cloud-friendly. Comfortable across languages and layers from firmware and circuit design to distributed cloud services, he brings pragmatic, performance-oriented design to complex systems. Outside of work he’s a multi-instrument musician and endurance athlete, a profile that hints at discipline and creative problem solving.
12 years of coding experience
15 years of employment as a software developer
Master of Science (MS) Electrical Engineering, Master of Science (MS) Electrical Engineering at Columbia UniversityBachelor of Science (BS) Electrical Engineering, Bachelor of Science (BS) Electrical Engineering at Villanova University
Github Skills (8)
python10
fn10
neuroimaging10
aws9
boto9
data-storage8
data-pipeline8
data-pipelines8
Programming languages (11)
Github contributions (5)
nipy/nipype
Jun 2014 - May 2016
Workflows and interfaces for neuroimaging packages
Role in this project:
Back-end Developer
Back-end DeveloperContributions:143 commits, 5 PRs, 46 comments in 1 year 11 months
Contributions summary:Daniel primarily contributed to the improvement and maintenance of the AFNI processing pipeline. They modified the input specifications of the TShift interface within the AFNI processing module, enabling support for string-based paths. The user also addressed and resolved issues within the DataSink interface, implementing features such as S3 bucket integration, MD5 checksums for data uploads, and local storage fallback mechanisms. These changes enhanced the flexibility, functionality, and reliability of the neuroimaging workflow.
workflowneuroimagingpythondata-scienceworkflow-engine
pintohutch/conf
Jan 2017 - Jan 2022
Contributions:59 pushes, 3 branches in 5 years
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