Mobile remittance app for immigrants sending money to Africa, Asia, LatAm
Taptap Send runs a mobile-first remittance app built on React Native, Kotlin and TypeScript, backed by a multi-cloud footprint (AWS, GCP, Azure) with Terraform and Kubernetes, and is now adding Datadog for observability. Hiring is accelerating and skews toward marketing, finance, legal and compliance over engineering, a sign the company is scaling corridors and regulatory coverage rather than rebuilding its core product.
Notable leadership hires: Head
Taptap Send is a privately held money transfer company headquartered in New York, founded in 2018, with 51–200 employees. Its mobile app lets immigrants send money to friends and family in Africa, Asia and Latin America within minutes, with no fees on most corridors. The company serves immigrant communities directly as consumers. It hires across the United States, United Kingdom, France, United Arab Emirates, Singapore, Nigeria and Mexico, reflecting a footprint spanning both sending and receiving markets.
Taptap Send builds with React Native, TypeScript, Kotlin, Spring, React and Python on PostgreSQL, running on AWS (ECS, EKS) plus GCP and Azure, with Terraform, Docker, Kubernetes, dbt and Redshift. It is adopting Datadog.
Taptap Send hires in the United States, United Kingdom, France, United Arab Emirates, Singapore, Nigeria and Mexico, with hiring velocity currently accelerating.
Current initiatives include an AI-enabled transformation agenda, a South Asia corridor growth strategy, payment corridor performance improvements, payout partner onboarding and systems architecture for its next growth stage.
Other companies in the same industry, closest in size
Taptap Send'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.