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Castleton Commodities International Tech Stack

Energy trading and infrastructure investing with cloud-native analytics

Investment Management Stamford, CT 201–500 employees Founded 2001 Privately Held

Castleton Commodities International operates an energy trading and infrastructure asset business built on Python services, cloud analytics (Snowflake, Power BI), and Kubernetes infrastructure. The tech stack reveals an engineering-heavy, infrastructure-first organization mid-migration: they're actively designing cloud-native architectures, automating infrastructure deployment, and transitioning legacy data systems — all while managing complex trading models and fundamental supply-demand forecasting. Hiring acceleration in engineering and senior-level roles signals scaling of both the modernization effort and core product capability.

Tech Stack 62 technologies

Core StackPython React Angular AWS Snowflake MATLAB Power BI Terraform Docker Kubernetes Datadog Crossplane Helm CloudFormation GitLab CI/CD Jenkins CI/CD Argo CD Karpenter AWS EKS AWS ECS Azure ExpressRoute AWS Direct Connect AWS Transit Gateway AWS WAF SolarWinds Cisco Catalyst Center AWS CDK AWS CodePipeline+31 more

What Castleton Commodities International Is Building

Challenges

  • Managing risk
  • Forecasting price movements
  • Understanding market behavior
  • Migration to k8s
  • Cloud-native modernization
  • Automation of infra deployment
  • Transitioning legacy data to new stack
  • Improving reliability of critical infrastructure
  • Designing resilient cloud-native architectures
  • Implementing robust disaster recovery plans

Active Projects

  • Cloud-native visualization and analytics tools
  • Python-based services and apis
  • Trading processes and analytical models
  • Asset business implementation
  • Fundamental supply and demand model development
  • Power pricing model improvement
  • New product configuration
  • Trading reference data architecture setup
  • Reporting and analytics solutions in power bi
  • Kubernetes infrastructure design using datadog, argo cd, crossplane, helm, karpenter

Hiring Activity

Accelerating15 roles · 5 in 30d

Department

Engineering
8
Data
2
Ops
2
Product
2

Seniority

Senior
8
Mid
3
Junior
1
Lead
1
VP
1
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About Castleton Commodities International

CCI is an energy markets trader and infrastructure asset investor founded in 2001, headquartered in Stamford, CT. The company combines fundamental research, advanced analytics, and trading operations across energy commodities and physical assets. Their technology platform supports real-time trading processes, pricing models (particularly in power markets), and risk management. The organization is executing a significant cloud and data-stack modernization: moving from legacy systems to Kubernetes-orchestrated services on AWS, adopting Snowflake for analytics, and building cloud-native reporting and visualization tools. Active projects span trading reference data architecture, supply-demand modeling, and disaster recovery infrastructure design.

HeadquartersStamford, CT
Company Size201–500 employees
Founded2001
Hiring MarketsUnited States, United Kingdom

Frequently Asked Questions

What is Castleton Commodities International's tech stack?

Core stack includes Python, React/Angular, AWS (EKS, ECS, WAF, Direct Connect), Snowflake, Kubernetes (Helm, Karpenter, Argo CD), Power BI, Terraform, Docker, MATLAB, Datadog, and CI/CD via GitLab and Jenkins.

What is Castleton Commodities International working on?

Active projects include Kubernetes infrastructure modernization, cloud-native analytics and visualization, Python-based trading APIs, supply-demand modeling, power pricing models, and disaster recovery architecture design.

How this profile is built

Castleton Commodities International's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →

This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.