DevOps Engineer

San Francisco, California, United States

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Not SpecifiedCompensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Sports Analytics, Betting, Fantasy SportsIndustries

Requirements

Candidates must have 5+ years of experience in a DevOps or DevSecOps role, with at least 5 years of experience building within AWS, including EKS, EC2, and RDS using Terraform or similar technologies. They should also have 5+ years of experience building CI/CD pipelines with Github Actions, Jenkins, or ArgoCD, and 5+ years of experience managing distributed systems with containerization tools like Kubernetes, Docker, and Helm. A minimum of 2 years of Python or Go experience is required, along with a deep understanding of distributed systems, SRE or DevOps experience in managing customer-facing systems in a 24/7 environment, and experience with machine learning pipelines. Preference will be given to candidates with experience in ML Ops or similar and knowledge of advanced statistical methods.

Responsibilities

The DevOps Engineer will develop, maintain, and implement Kubernetes clusters at scale for ML workloads. They will collaborate with Data Science and Data Engineering teams to optimize and scale workloads on Kubernetes using CI/CD, automation tools, and scripting languages. The role involves developing and implementing specialized infrastructure to improve service reliability and scalability, pioneering and enforcing best practices for software deployment and code management, monitoring systems, responding to incidents to maintain system SLO/SLA, and reviewing production incidents. On-call work will be required as needed.

Skills

Kubernetes

AWS

EKS

EC2

RDS

Terraform

Terragrunt

CI/CD

Github Actions

Jenkins

ArgoCD

Docker

Helm

Python

DevOps

DevSecOps

System Monitoring

Incident Response

SLO/SLA Management

Containerization

Distributed Systems

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.

San Francisco, CaliforniaHeadquarters

2014Year Founded

$6.5MTotal Funding

EARLY_VCCompany Stage

Fintech, AI & Machine Learning, Financial ServicesIndustries

51-200Employees

Benefits

Remote Work Options

Risks

Increased competition from AI-driven startups could erode Swish Analytics' market share.

Consumer privacy concerns may impact Swish Analytics' data collection practices.

Potential regulation of sports betting advertising could affect Swish Analytics' revenue streams.

Differentiation

Swish Analytics uses proprietary algorithms for accurate sports predictions and optimized lineups.

The company is an Authorized MLB Data Distributor, enhancing its credibility in sports betting.

Swish Analytics offers a subscription model with free trials, attracting diverse user segments.

Upsides

Increased legalization of sports betting in the U.S. expands Swish Analytics' market opportunities.

The rise of AI-driven personalized betting experiences aligns with Swish Analytics' machine learning expertise.

Growing interest in micro-betting offers Swish Analytics a chance to expand its offerings.

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