Cloud Security Engineer

San Francisco, California, United States

Apply with AI Apply

Not SpecifiedCompensation

Junior (1 to 2 years)Experience Level

Full TimeJob Type

UnknownVisa

Sports Analytics, Betting, Fantasy Sports, Predictive Analytics, Data ProductsIndustries

Requirements

Candidates should possess 5-8 years of hands-on experience in AWS security, with strong knowledge of IAM roles and policies, AWS security services such as GuardDuty, Security Hub, and Inspector, VPC security, subnet segmentation, and security groups, along with familiarity with tools like BurpSuite, OWASP ZAP, and Datadog. Proficiency in Infrastructure-as-Code (IaC) using tools like Terraform or CloudFormation is required, as well as experience managing infrastructure policies through Git repositories. Candidates should also have experience with container security best practices and Kubernetes cluster security.

Responsibilities

The Cloud Security Engineer will be responsible for implementing and maintaining cloud security posture management (CSPM) tools and frameworks, integrating security tools into CI/CD pipelines, automating compliance and security checks, managing infrastructure and security policies through Git repositories, and responding to security incidents in AWS environments. They will also integrate security tools into CI/CD pipelines, automate compliance and security checks, and provide security expertise to developers, cloud engineers, and security teams, while contributing to the overall security of the company’s AWS infrastructure and applications.

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.

Land your dream remote job 3x faster with AI

Try Jobo Free