SaaS trading, risk and market-access platform for capital markets firms
Trading Technologies runs a multi-asset SaaS trading platform built on a C++17 core profiled with GDB, Valgrind and gprof, alongside Python, Go and TypeScript services on AWS and Kubernetes. Retiring VMware while adopting Backstage and GitOps tooling (ArgoCD, Flux) points to a push off legacy datacenters toward a cloud-native developer platform. Senior-heavy engineering hiring matches pain points around memory bottlenecks and ultra-low latency.
Notable leadership hires: Director of Procurement, Tax Director
Trading Technologies, founded in 1994 and headquartered in Chicago, Illinois, provides a SaaS platform to the global capital markets industry. The platform covers trade execution, order management, market data, analytics, surveillance, risk, clearing and post-trade services across listed derivatives, fixed income, FX and crypto. Clients include banks, brokers, hedge funds, proprietary traders, CTAs, commercial hedgers and exchanges. The privately held company employs 201–500 people and hires in the United States, United Kingdom and Czechia.
Its stack includes C++17, Python, Go, TypeScript and React, running on AWS, Red Hat Enterprise Linux, Docker and Kubernetes, with Terraform, AWS CDK, ArgoCD and Flux. Business tools include Salesforce, NetSuite and Coupa.
Yes, VMware is being replaced, with job posts citing removal of a legacy VMware datacenter and modernization of the company's AWS footprint. Backstage is also being adopted.
Trading Technologies hires in the United States, United Kingdom and Czechia, with an emphasis on senior engineering plus sales and finance roles, including procurement and tax leadership.
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Trading Technologies'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.