DevOps Engineer
SGNLSalary not specified
- Full Time
- Junior (1 to 2 years)
€75,000 – €85,000Compensation
Junior (1 to 2 years)Experience Level
Full TimeJob Type
UnknownVisa
Sports Analytics, Data & Analytics, Betting, Fantasy SportsIndustries
Requirements
Candidates should possess 4+ years of experience in a DevOps or DevSecOps role, 4+ years of experience building within AWS, including EKS, EC2, RDS, and other common AWS services using Terraform, Terragrunt or similar technologies, 4+ years of experience 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, a deep understanding of distributed systems including storage, networking, and security, previous SRE or DevOps experience in managing customer-facing systems in a 24/7 environment, and experience with machine learning pipelines.
Responsibilities
The DevOps Engineer will develop, maintain, and implement Kubernetes clusters at scale suitable 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 on production incidents, and provide on-call support as needed.
Skills
AWS
EKS
EC2
RDS
Terraform
Terragrunt
Github Actions
Jenkins
ArgoCD
Kubernetes
Docker
Helm
Python
Go
Distributed Systems
Networking
Security
SRE
Machine Learning Pipelines
Swish Analytics
Sports analytics and optimization tools provider
About Swish Analytics
Swish Analytics specializes in sports analytics and optimization tools for daily fantasy sports and sports betting, focusing on major U.S. leagues like the NFL, MLB, NBA, and NHL. The company uses an advanced machine learning system to analyze large datasets, providing accurate sports predictions and optimized lineups. This helps users, including individual bettors and professional operators, make informed decisions about their bets and fantasy picks. Swish Analytics differentiates itself by being an Authorized MLB Data Distributor, establishing trust in the sports betting community. Operating on a subscription-based model, users can access various levels of tools and analytics, starting with a free trial. The goal of Swish Analytics is to maximize return on investment for clients by identifying the best bets and balancing risk and reward for long-term success.
Key Metrics
San Francisco, CaliforniaHeadquarters
2014Year Founded
$6.5MTotal Funding
EARLY_VCCompany Stage
Fintech, AI & Machine Learning, Financial ServicesIndustries
51-200Employees