Mountain America Credit Union (MACU) operates a 1,000+ person credit union headquartered in Utah with a tech stack weighted heavily toward Microsoft (Power BI, Fabric, Purp, Power Platform) and financial-services tooling (Encompass, Symitar, Chex, Verafin). The project and pain-point lists reveal a compliance-and-fraud-first engineering culture: active work spans fraud detection systems, risk-assessment automation, audit modernization, and control-gap remediation. Hiring is ops-heavy (40% of roles) with a junior-skewed mix, indicating a scaling operations function tasked with new fraud and risk capabilities.
Mountain America Credit Union is a nonprofit credit union founded in 1934 and headquartered in Sandy, Utah, serving members across the United States. The organization provides banking, lending, and investment products to individuals and businesses, including commercial loans, SBA loans, checking, savings, and business accounts. With 1,000–5,000 employees, MACU operates as a regulated financial institution (NCUA-insured, Equal Housing Lender) and is currently modernizing its technology infrastructure to strengthen fraud detection, risk management, and loan origination processes. Active work includes development of indirect lending and business member services products, alongside foundational shifts toward advanced monitoring, audit automation, and cybersecurity capabilities.
MACU's stack is anchored in Microsoft tooling (Power BI, Tableau, Fabric, Power Platform, Purview) and financial-services platforms (Symitar, Encompass, Chex Systems, Verafin). Infrastructure includes Kubernetes, Terraform, and ServiceNow for operations automation.
Active projects span indirect lending product development, business member services, advanced fraud detection systems, risk assessment automation, audit modernization, cybersecurity capability roadmaps, and fraud alerting platform enhancements. A key pattern: cross-functional effort to reduce control gaps and improve alert accuracy.
Mountain America Credit Union'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.