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Full-Time

Site Reliability Engineer

Confirmed live in the last 24 hours

Swish Analytics

Swish Analytics

51-200 employees

Sports analytics and optimization tools provider

Data & Analytics
AI & Machine Learning
Consumer Goods

Compensation Overview

$100k - $150kAnnually

Mid

Remote in USA

Category
DevOps & Infrastructure
Site Reliability Engineering
Required Skills
Bash
Kubernetes
Python
Git
Development Operations (DevOps)
Requirements
  • 3+ years of experience working in an SRE leaning DevOps or full SRE roles
  • 3+ years building CICD pipelines with Github Actions, Gitlab CICD, or similar
  • Extensive experience with Kubernetes
  • Experience in managing customer-facing systems in a 24/7 environment including escalations
  • Experience triaging and escalation policies/protocols
  • Strong communication and documentation skills
  • Comfortable with scripting languages like Bash, Python, or similar
Responsibilities
  • Support production systems and help triage issues during live sporting events
  • Monitor the system and respond to incidents to maintain system SLO/SLA, review and follow up production incidents
  • Write and review code, develop documentation, and debug problems, live, on complex distributed systems
  • Optimize and facilitate incident response, conduct root cause analysis and blameless retrospectives
  • Work closely with technical teams to implement, optimize, maintain, scale and debug workloads on Kubernetes using CI/CD, automation tools and scripting languages to deliver tools/software to improve the reliability and scalability of services

Swish Analytics specializes in sports analytics and optimization tools specifically for daily fantasy sports and sports betting in the U.S. market, focusing on major leagues like the NFL, MLB, NBA, and NHL. The company uses an advanced machine learning system to analyze large datasets, providing users with accurate sports predictions and optimized lineups. This helps individual bettors and fantasy sports players make informed decisions about their bets and picks. Swish Analytics differentiates itself from competitors by being an Authorized MLB Data Distributor, which enhances its credibility and access to data. The company operates on a subscription-based model, allowing users to start with a free trial and choose from various plans for different levels of access. The goal of Swish Analytics is to maximize return on investment for its clients by identifying not only winning bets but also the most strategic bets that balance risk and reward for long-term success.

Company Stage

Series C

Total Funding

$29.8M

Headquarters

San Francisco, California

Founded

2014

Growth & Insights
Headcount

6 month growth

3%

1 year growth

7%

2 year growth

44%
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Simplify's Take

What believers are saying

  • Swish Analytics' machine learning system can significantly improve users' ROI by identifying the smartest bets, making it highly attractive for serious sports bettors.
  • Partnerships with major leagues and sportsbook platforms can lead to increased credibility and market reach.
  • The company's focus on major U.S. sports leagues ensures a large and engaged user base, providing ample growth opportunities.

What critics are saying

  • The highly competitive nature of the sports analytics market means Swish Analytics must continuously innovate to stay ahead.
  • Reliance on partnerships with major leagues and platforms could pose risks if these relationships are disrupted.

What makes Swish Analytics unique

  • Swish Analytics leverages advanced machine learning to provide highly accurate sports predictions, setting it apart from competitors who rely on traditional statistical methods.
  • Their partnership with Major League Baseball as an Authorized MLB Data Distributor gives them exclusive access to high-quality data, enhancing the accuracy of their analytics.
  • The subscription-based model with a free trial option makes their advanced tools accessible to a wide range of users, from casual bettors to professional operators.