Buy-now-sell-later property financing for Australian homebuyers
Bridgit finances home purchases in Australia using a BNPL model, letting buyers acquire property before selling their current home. The stack—Node.js + TypeScript on AWS + PostgreSQL, paired with Databricks + Microsoft Fabric for data—reflects a fintech built for compliance-heavy lending: infrastructure-as-code (Terraform, Pulumi, Kubernetes) and observability (Prometheus, Grafana, Datadog) run throughout. Hiring acceleration across engineering (8 roles) and finance (6) signals scaling of credit operations; the pain-point list (manual credit workflows, broker network expansion, credit automation) shows the core bottleneck is lending operations, not just platform engineering.
Notable leadership hires: AI Lead, Chief People Officer
Bridgit operates a buy-now-sell-later mortgage product in Australia, targeting homeowners who want to purchase without first selling an existing property. The company offers a 5-minute online application with 24-hour approval. Founded in 2021 and based in Sydney, Bridgit employs 51–200 staff, with active development across core banking infrastructure, payment gateway integration, user wallets, and risk/compliance frameworks. The business model requires tight integration between fintech platform (approval, underwriting) and lending operations (broker networks, credit decisioning). Hiring is accelerating in both engineering and finance, with notable leadership additions including an AI Lead and Chief People Officer.
Node.js, TypeScript, AWS (CDK, RDS, ECS, Lambda), PostgreSQL, and Python for backend. Databricks and Microsoft Fabric handle data pipelines. Infrastructure-as-code via Terraform and Pulumi; observability via Prometheus, Grafana, and Datadog.
Bridgit is based in Sydney, New South Wales, Australia. Active hiring also in the Philippines, indicating offshore engineering operations alongside Australian-based leadership and finance teams.
Bridgit'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.