Staff API & Partner Integrations Engineer, Trustly

• Typically 10+ years building and operating production software systems, including several years owning systems at staff level or equivalent
• Hands-on experience with payments, banking or other financial systems where correctness, auditability and uptime matter most
• You have carried the pager for systems you built, and improved them because of it
• A track record of taking over an unfamiliar, business-critical codebase and becoming its owner
• Daily use of AI coding tools (such as Claude Code, Cursor or Copilot), with clear views on where they help and where human review has to hold
• Strong distributed systems design: data consistency, idempotency, concurrency
• Clear writing. Your design docs and incident write-ups are the ones other people reuse
• Production experience with Java or a comparable backend language
• Experience building integrations with bank APIs, core banking or online banking providers, or data aggregators, including consent and token management

Desirable

• Trustly moves money between consumers, merchants and banks across the US
• We are building our US engineering hubs in San Francisco and New York, starting with a small group of staff engineers who will take technical ownership of our most critical systems
• You will own API and Partner Integrations end to end: its architecture, its reliability, its operation in production, and where it goes next
• You'll own our API-based connectivity: direct bank APIs (FDX), core and online banking provider APIs, and third-party aggregators
• That includes consent, tokens and certification with each partner, and readiness for open banking rules such as Section 1033
• You'll start by learning a mature, business-critical codebase fast, using AI tools to map it and writing the documentation others will build from
• Within your first year, you'll help hire and lead the team that owns it with you
• Own the architecture, service contracts and production health of API and Partner Integrations
• Own the operation of your services: on-call, SLAs, incident response and root-cause fixes. There is no separate operations team; the team that builds the service runs it
• Meet the observability standards set for every service, and make the service measurably better each quarter
• Use AI tools to understand existing code quickly, then turn what you learn into design docs, interface definitions and runbooks that engineers and AI agents can work from
• Partner with the engineers who run these systems today, across the Americas and Europe, to transfer knowledge
• Direct AI-assisted implementation: write and close implementation plans, review output against requirements, and own integration testing
• Help hire and shape the team that will own this domain