Dougal Ballantyne is a seasoned cloud and HPC leader with 13 years of recent experience and over two decades in technology, currently advising Oracle Cloud Infrastructure while building and investing in early-stage startups from Seattle. He blends hands-on engineering—contributions to projects like AWS ParallelCluster and leadership roles across AWS (Lambda, CloudFormation, Wavelength, API Gateway, Batch)—with product and strategic thinking to design and deploy large-scale, production systems. Fluent in English and Dutch, he excels at customer-facing technical conversations with senior stakeholders and translating complex protocol and network analysis into pragmatic solutions. Dougal’s background spans telecoms, HPC R&D, and cloud-native infrastructure, and he’s known for pairing energetic, trustworthy leadership with deep operational experience managing infrastructure-as-code and edge services. An unexpected detail: beyond advising and building, he’s an investor and proud dog dad, signaling a founder-friendly, people-oriented approach to technical challenges.
AWS ParallelCluster is an AWS supported Open Source cluster management tool to deploy and manage HPC clusters in the AWS cloud.
Role in this project:
DevOps Engineer & Cloud Engineer
Contributions:12 releases, 230 commits, 24 PRs in 3 years
Contributions summary:Dougal's commits primarily focused on enhancing infrastructure and supporting cloud-related features within the AWS ParallelCluster project. Contributions include adding support for proxy configuration using boto.config, enabling the use of an existing VPC security group, and implementing features related to root volume size and EBS volume management. They also worked on general improvements, such as updates for the Chef client and various AMI updates.
A Serverless Application that creates Lambda function to use as an authorizer in Amazon API Gateway for HTTP Basic Auth and a DynamoDB tables for users.
Contributions:1 release, 4 commits, 1 PR in 1 year 6 months
api-gatewayserverlessaws-lambdaawslambda
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