United States onlyCompany Overview
Swish Analytics is a sports analytics, betting and fantasy startup building the next generation of predictive sports analytics data products. We believe that oddsmaking is a challenge rooted in engineering, mathematics, and sports betting expertise; not intuition. We're looking for team-oriented individuals with an authentic passion for accurate and predictive real-time data who can execute in a fast-paced, creative, and continually-evolving environment without sacrificing technical excellence. Our challenges are unique, so we hope you are comfortable in uncharted territory and passionate about building systems to support products across a variety of industries and enterprise clients.
Responsibilities:
- Use your experience to develop, maintain and implement Kubernetes clusters at scale that are ready for heavy/spiky ML workloads
- Work closely with Data Science and Data Engineering teams to implement, optimize and scale workloads on Kubernetes using CI/CD, automation tools and scripting languages
- Help Data Science and Data Engineering develop and implement specialized infrastructure to deliver tools/software to improve the reliability and scalability of services
- Pioneer, implement, and enforce best practices for software deployment and code management
- Monitor the system and respond to incidents to maintain system SLO/SLA, review and follow up production incidents
- Provide on call work as needed
Qualifications:
- 4+ years of experience working in a DevOps or DevSecOps role
- 4+ years building within AWS, including EKS, EC2, RDS, and other common AWS services using Terraform, Terragrunt or similar technologies
- 4+ years building CICD pipelines with Github Actions, Jenkins and ArgoCD
- 4+ years of experience managing, provisioning and maintaining distributed systems with containerization tools, including Kubernetes, Docker, Helm
- 2+ years of Python or Go experience
- Have a deep understanding of distributed systems including storage, networking, and security
- Have previous SRE or DevOps experience in managing customer-facing systems in a 24/7 environment
- Have worked with machine learning pipelines
Preferred:
- Experience with ML Ops or similar
- Knowledge in advanced statistical methods
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