Summary
Join our team at Code for Science & Society as a Cloud Engineer to ensure the reliable operation of our infrastructure, explore emerging technologies, and collaborate with open source communities.
Requirements
- Experience with deploying applications on cloud infrastructure
- Experience deploying and developing with Linux container-based technologies, such as Docker and Kubernetes
- Experience with continuous integration services (e.g. Circle CI, GitHub workflows)
- Experience developing tools in a general purpose programming language (eg. Python)
- Experience collaborating and coordinating work via online platforms, such as GitHub, GitLab, or BitBucket, and distributed revision control
- Experience working with distributed service teams that use asynchronous methods of communication
- Working hours that overlap with US Pacific time zones
Responsibilities
- Ensures the reliable operation of the 2i2c infrastructure (leveraging production-ready cloud-based tools such as JupyterHub, BinderHub, and Dask)
- Attends support-facing tickets as part of a support rotation process/group (this is not your primary task, although it is currently a shared load among engineers)
- Explores emerging technologies in the Cloud / DevOps space, design and implement cloud computing architecture in partnership with our team
- Participates in upstream open source communities we rely on (such as JupyterHub, BinderHub, Dask, etc) in partnership with the established leaders of those communities and collaborate with the Community Lead in the education and outreach around cloud computing
- Work with a distributed and global team - team members are given a lot of autonomy, and expected to be proactive (we do not expect people to carry a pager) at communicating with one another and working with others to allocate effort that will maximize our impact
Preferred Qualifications
- Experience with major cloud providers
- Experience in programming and software engineering with a track record of leadership in open, collaborative projects with broad community adoption
- Experience working on geographically distributed open-source projects
- Experience with the Jupyter ecosystem and other tools for interactive computing
- Evidence of existing connections and relationships in the worldwide ecosystem of open source software for data-intensive research and the ability to establish new ones
- Experience with common data science methods, platforms, workflows, and infrastructures; with data management systems, practices, and standards; and the capacity to gain familiarity with new related topics
- Experience engaging with highly technical researchers across a variety of methodological fields, research domains, and computational platforms
- Experience building and maintaining continuous deployment pipelines
- Interpersonal skills to work with researchers and students. Include having skills to communicate complex information in a clear and concise manner both verbally and in writing
