Staff Backend Engineer – Financial Ledger

About the role

This position is based in Luxembourg and sits at the core of a rapidly evolving financial infrastructure that powers a modern travel‑tech SaaS platform. You will take technical ownership of the Financial Core system, ensuring the accuracy of revenue recognition, payments, ledger events, and profit calculation across a distributed environment.

Key responsibilities

• Guide the evolution of the Financial Core system, challenging assumptions and aligning architecture with long‑term scalability.
• Validate lifecycle flows, reconciliation logic, and financial correctness across distributed services.
• Design and maintain ledger‑style financial models, including revenue recognition, cost allocation, and multi‑party transaction flows.
• Enforce strict financial invariants within an event‑driven architecture to guarantee deterministic and auditable outcomes.
• Collaborate with finance and engineering leadership to translate business rules into robust technical implementations.
• Identify gaps in the current production architecture and drive improvements toward future iterations.
• Support reliability and scalability of payment, billing, and financial reporting domains.
• Contribute hands‑on to backend development while shaping long‑term architectural direction.

Required profile

• 8+ years of backend engineering experience with ownership of financial, billing, or transactional systems.
• Proven experience building or maintaining payment systems, billing engines, ERP solutions, or subscription infrastructure.
• Deep expertise in ledger systems, reconciliation mechanisms, and deterministic financial correctness.
• Strong understanding of revenue recognition, liabilities, cost structures, and multi‑party money flows.
• Experience evolving complex production systems without disrupting existing functionality.
• Hands‑on engineering mindset capable of both designing architecture and implementing critical backend components.

Required skills

• Python
• PostgreSQL (relational data modeling)