Real-time payments and fraud detection for banks and credit unions
Early Warning operates the infrastructure behind Zelle and Paze, processing payments across 2,500+ bank and credit union brands. The tech stack is heavily data-engineering-focused (PySpark, Scala, Hive, Kafka-pattern tooling) with Java/Spring Boot on the backend and Kubernetes orchestration — a profile built for high-throughput, distributed transaction processing. Active hiring is concentrated in engineering and data roles, and the project list reveals a hard pivot toward cross-border payments and digital assets alongside aggressive fraud-detection model work, indicating both organic platform expansion and regulatory pressure.
Notable leadership hires: Director Engineering, Communications Director, Strategic Account Director, Social Media Director, HR Business Management Director
Early Warning Services is a financial services technology company founded in 1990 and headquartered in Scottsdale, Arizona. The company operates the network intelligence layer for the U.S. banking ecosystem, providing real-time payments (Zelle for peer-to-peer), online checkout (Paze), risk mitigation, and fraud detection across its partner network of 2,500+ financial institutions. The business sits between consumer protection and bank operations, managing transaction integrity, compliance with AML and sanctions rules, and increasingly, digital asset and cross-border payment flows. With 1,001–5,000 employees, the organization is scaling to handle regulatory complexity and volume growth in real-time payment flows.
PySpark, Scala, and Hive for data processing; Java and Spring Boot for backend services; Kubernetes for orchestration; Salesforce for CRM; Splunk for monitoring. Frontend includes Node.js, Objective-C, and Android/iOS native code.
Cross-border digital asset and consumer money movement platforms; real-time fraud detection and risk assessment models; wallet and transaction orchestration; AML and sanctions compliance tooling; and data ETL and storage infrastructure scaling.
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