Core technology provider for the Farm Credit System's lending infrastructure
Farm Credit Financial Partners runs technology and financial services for four agricultural credit associations serving 62,000+ members. The tech stack is heavily weighted toward security (Sentinel, Defender XDR, CrowdStrike, CyberArk) and data infrastructure (Lakehouse, Databricks, Azure Data Factory, Splunk), with active projects spanning SIEM/XDR implementation, IAM/PAM architecture, and legacy platform modernization. Hiring velocity is accelerating across data, engineering, and security—a signal that FPI is scaling cloud-native and threat-detection capabilities to handle the compliance and scale demands of managing $40B in Farm Credit assets.
Notable leadership hires: Tech Lead
Farm Credit Financial Partners is a customer-owned service organization providing technology products and financial services exclusively to the Farm Credit System. Founded in 1995, FPI supports four agricultural credit associations (ACAs) spanning Maine to California, serving over 62,000 members managing $40 billion in Farm Credit assets. The company operates a 201–500-person organization from Springfield, Massachusetts, focused on lending infrastructure, data governance, and enterprise security. Current operational priorities include modernizing legacy data platforms, deploying large-scale security monitoring (SIEM/XDR), and implementing infrastructure-as-code security automation across cloud and on-premises environments.
FPI's stack includes Databricks, Azure Data Factory, and Lakehouse for data infrastructure; Microsoft Sentinel, Defender XDR, and CrowdStrike for security; Okta and CyberArk for identity and access management; and Splunk for monitoring. They are actively adopting Databricks for their lakehouse strategy.
Core projects include lakehouse and Databricks adoption, legacy data platform modernization, large-scale SIEM/XDR implementation, scalable IAM/PAM architecture design, infrastructure-as-code security automation, data governance, and ETL pipeline optimization.
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