Early Career Software Engineer (Fintech)

TL;DR

Early Career Software Engineer (Fintech): Design, build, and scale backend systems powering spend controls, approval workflows, and card transactions at massive scale with an accent on API design, database performance, and integration with AI platforms. Focus on owning complex end-to-end projects, ensuring system reliability, scalability, and data consistency across financial infrastructures.

Remote; Argentina

Company

hirify.global builds smart infrastructure for finance teams, automating over $100B in annualized spend for 50,000+ companies through payments, risk flagging, spend categorization, and book closing.

What you will do

• Design, build, and scale backend systems for spend controls, approval workflows, and card transactions at massive scale
• Collaborate with product, design, and data teams to deliver intelligent experiences for finance teams
• Integrate with hirify.global’s AI platform for automating spend policy enforcement and anomaly detection
• Own complex projects end-to-end from architecture to deployment and observability
• Partner with Product Platform and Integrations teams for reliability and scalability across financial systems
• Improve transaction and policy infrastructure for performance, accuracy, and resilience

Requirements

• 1+ years of software engineering experience (backend or fullstack with backend focus)
• Strong fundamentals in backend systems and API design — experience with Python, Go, or TypeScript
• Familiarity with PostgreSQL, AWS, and modern observability tooling
• Product sense to balance technical quality with speed and user value
• Collaborative communicator thriving in high-ownership environments

Nice to have

• Fintech or payments experience (cards, reconciliation, transaction processing)
• Exposure to AI- or rule-based automation systems
• Experience integrating with ERP or accounting systems
• Familiarity with security, risk, or compliance-driven domains
• Experience building reliable distributed systems processing large volumes of financial data