Brigit operates a consumer fintech platform focused on cash advances and credit products for mid-market income Americans. The tech stack—React, React Native, TypeScript, Java, GCP, GraphQL, dbt, Snowflake, Spark, and MLflow—reveals an engineering organization building mobile-first experiences backed by data infrastructure and machine learning. Active hiring is concentrated in product and data roles, with a notable focus on scaling credit underwriting models and expanding the credit portfolio beyond cash advances.
Notable leadership hires: Head of Credit, HR Director
Brigit is a fintech platform serving everyday Americans with transparent financial products centered on cash advances and line-of-credit offerings. The company operates from New York and was founded in 2017. With 51–200 employees, the organization is scaling product and data functions to support expansion into broader credit products and improved underwriting capabilities. The platform is structured around mobile access (iOS, Android), real-time data pipelines (Airflow, Spark, dbt, Snowflake), and analytics-driven decision-making (Amplitude, Metaplane). Current operational priorities include improving user retention, optimizing conversion across the product funnel, and building ML-driven underwriting models to manage a growing credit portfolio.
Brigit's primary stack includes React and React Native for frontend, TypeScript and Java for backend, GCP for cloud infrastructure, GraphQL for APIs, dbt and Snowflake for data transformation, Spark and MLflow for ML operations, and Airflow for orchestration.
Key projects include scaling the credit portfolio beyond cash advances, building ML-driven underwriting models, designing credit products using cash-flow data, optimizing in-product user journeys, and maintaining dbt transformation infrastructure. The company is also evaluating M&A opportunities.
Brigit'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.