Non-QM mortgage lender serving wholesale and direct consumers across 39+ states
Acra Lending operates a private mortgage lending platform focused on non-qualified-mortgage (non-QM) products, serving both mortgage professionals and consumers across 39 states. The tech stack reveals a data-first operational model: SQL Server + Snowflake + dbt form the core analytics spine, layered with Salesforce for CRM and Finastra for lending-specific domain logic. Current project focus—ETL optimization, cloud database migration, and disaster recovery—paired with pain-point density around data governance and compliance, suggests the company is investing heavily in infrastructure maturity and regulatory risk management as it scales.
Acra Lending is a privately held residential mortgage lender headquartered in Irvine, California, licensed as an NMLS mortgage lender (ID #144549) offering direct lending, wholesale lending, correspondent purchasing, and loan servicing. The company specializes in alternative income documentation, non-prime and subprime lending, bank statement loans, and jumbo mortgages—product categories that require higher data rigor and risk modeling than traditional QM lending. Operations span 39 U.S. states. The workforce of 201–500 employees is currently hiring most aggressively in sales and finance roles, with smaller engineering, data, and support teams supporting the lending and servicing operations.
Acra Lending runs Salesforce (CRM), SQL Server, Snowflake, and dbt (analytics), Finastra (lending domain), AWS RDS, Power BI, Tableau, Boomi (integration), and Azure-based security (Defender, Sentinel, Purview). Active projects include ETL optimization and cloud database migration.
Acra Lending specializes in non-QM lending, bank statement loans, stated income, alternative income documentation, subprime/non-prime mortgages, jumbo loans, second mortgages, and private money loans. It serves mortgage professionals and direct consumers across 39 states.
Acra Lending'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 →
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