IT consulting for financial services modernization and real-time data systems
Enterprise Engineering Inc. operates a financial-services-focused consulting practice built on Java, Kafka, Kubernetes, and cloud platforms (AWS, Azure, GCP). The tech stack reflects deep engagement with real-time systems: Kafka for event streaming, Databricks and StreamSets for data pipelines, MuleSoft for integration, and Cognos/Power BI for analytics. Current project focus—fraud detection, P&L engines, and distributed systems—combined with pain points around latency and high-throughput market risk, signals heavy involvement in quantitative finance modernization rather than general IT consulting.
Enterprise Engineering Inc. is a consulting firm founded in 1995 serving financial institutions, wealth managers, and healthcare providers. The firm specializes in application modernization, cloud transformation, open banking readiness, and enterprise data solutions. Their client base consists of large financial institutions and C-suite stakeholders (CIOs, CEOs, COOs). The company operates from New York with 51–200 employees and is actively hiring across engineering, data, marketing, and security roles, with accelerating velocity in the United States and Canada.
EEI runs on Java, Kafka, Kubernetes, Docker, Spring Boot, and cloud platforms (AWS, Azure, GCP). Data work uses Databricks, StreamSets, and MuleSoft. Analytics layers include Cognos, Power BI, and Tableau CRM. Project delivery and team collaboration rely on Jira and EazyBI.
Active projects include next-generation fraud detection solutions, real-time distributed systems, real-time P&L engines, and a Jefferies knowledge graph. Core challenges center on latency-sensitive systems, high-throughput market risk computation, and data integration at scale.
Other companies in the same industry, closest in size
Enterprise Engineering Inc. (EEI)'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.