Swish Analytics

Cloud Security Engineer

San Francisco, California, United States

Not SpecifiedCompensation
Junior (1 to 2 years)Experience Level
Full TimeJob Type
UnknownVisa
Sports Analytics, Betting, Fantasy Sports, Predictive Analytics, Data ProductsIndustries

Job Title: AWS Security Engineer

Salary: Starting at $140,000 - $182,000 DOE

Location Type: 100% Remote

Employment Type: Full-time


Company 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 consumer/enterprise clients.


Position Overview

This is a 100% remote position for an AWS Security Engineer.


Key Responsibilities

AWS Security (5-8 years of hands-on experience)

  • Strong knowledge of IAM (roles, policies, least privilege)
  • Experience with AWS security services (e.g., GuardDuty, Security Hub, Inspector, Macie, KMS, CloudTrail, WAF, Shield)
  • Familiarity with VPC security, subnet segmentation, NACLs, and security groups
  • Familiarity with security and logging tools such as BurpSuite, OWASP ZAP, CrowdStrike, Datadog, etc.
  • Deep understanding of AWS Well-Architected Framework, especially the Security Pillar
  • Experience implementing and maintaining cloud security posture management (CSPM) tools and frameworks
  • Proficient in Infrastructure-as-Code (IaC) using tools such as Terraform, CloudFormation, or AWS CDK
  • Experience managing infrastructure and security policies through Git repositories

SecDevOps / DevSecOps Experience

  • Integrating security tools into CI/CD pipelines (e.g., Snyk, Checkov, Trivy, SonarQube)
  • Automating compliance and security checks
  • Experience with Github
  • Container security best practices (ECR, ECS, EKS, etc.)

Kubernetes and Container Security Expertise

  • Experience securing Kubernetes clusters (EKS or self-managed)
  • Knowledge of network policies, RBAC, Pod Security Standards, and runtime security

General Skills

  • Proficiency with scripting languages like Python, Bash, or PowerShell for automation and security tooling
  • Experience with security incident response in AWS environments, including detection, analysis, and mitigation

Preferred Certifications (Not Required)

  • AWS Certified Security – Specialty
  • AWS Certified Solutions Architect – Associate or Professional
  • Certified DevSecOps Professional (e.g., DevSecOps Foundation)
  • GIAC Cloud Security Automation (GCSA) or similar
  • Certified Kubernetes Security Specialist (CKS)

Soft Skills

  • Strong problem-solving and analytical skills
  • Ability to work cross-functionally with developers, cloud engineers, and security teams
  • Clear communication skills with both technical and non-technical stakeholders
  • Self-starter mindset with the ability to work in a fast-paced, agile environment

Company Culture & Policies

Swish Analytics is an Equal Opportunity Employer. All candidates who meet the qualifications will be considered without regard to race, color, religion, sex, national origin, age, disability, sexual orientation, pregnancy status, genetic, military, veteran status, marital status, or any other characteristic protected by law.

The position responsibilities are not limited to the responsibilities outlined above and are subject to change. At the employer’s discretion, this position may require successful completion of background and reference checks.

Skills

AWS Security
IAM
GuardDuty
Security Hub
Inspector
Macie
KMS
CloudTrail
WAF
Shield
VPC security
Subnet segmentation
NACLs
Security groups
BurpSuite
OWASP ZAP
CrowdStrike
Datadog
AWS Well-Architected Framework
CSPM
Terraform
CloudFormation
AWS CDK
Git
Snyk
Checkov
Trivy
SonarQube
Github
ECR
ECS
EKS
Kubernetes security
Network policies

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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