Financial Connections TechOps Integration Reliability Engineer

Job description

Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world's largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career. About the team Stripe's Financial Connections group is building technology that expands the scope of problems we tackle beyond card payments. We aspire to enable individuals and businesses to securely share their financial data to improve their product experiences. With more high-quality customer financial data, both Stripe and our users can offer better products to their customers. We have ambitious goals to enable all global businesses to run their entire financial life cycle on Stripe, and we want you to be part of our story. Technical Operations roles in Financial Connections are a dynamic and critical component of the organization's success. We sit at the intersection of product and platform engineers, financial partners, and AI systems—connecting them to ensure that nothing is lost in translation and that the systems underpinning Stripe’s open banking ecosystem remain reliable at scale. What you'll do Operate, maintain, and expand banking integrations Become a domain expert in open banking, financial data access, and data aggregation—the systems, the partners, and the failure modes Own integration health end-to-end: triage degradations, diagnose root causes, coordinate with financial partners, and drive resolution with urgency Handle technical inquiries from internal Stripe teams and external partners, often requiring deep investigation and targeted code or configuration changes Build and launch new financial partner integrations using agentic tooling Conduct complex data analysis to investigate product performance, scope partner requirements, and surface reliability insights Build agentic systems and automation Design, build, and ship LLM-powered agentic workflows that streamline and automate high-volume operational tasks Operate as a Human-in-the-Loop reviewer for automated processes that carry meaningful risk, applying your domain expertise where automation alone isn't sufficient Identify inefficient or manual processes and eliminate them through AI-powered automation, code changes, or driving complex process improvements across Stripe Partner and scale Serve as a technical point of contact for financial partners—troubleshooting issues, aligning on product usage, and managing relationships with clear communication Provide authoritative technical context to cross-functional partners including product, partnerships, compliance, and legal Design and improve anomaly detection systems and observability tooling to ensure data flowing through partner and internal systems is reliable and correct Who you are We're looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement. TechOps IRE roles combine software engineering, data analysis, AI systems development, and cross-functional partner management. The ideal candidate brings experience across technical systems, data investigation, and complex project ownership—and finds genuine satisfaction in making things run reliably at scale through both human judgment and intelligent automation. Successful candidates may index more highly in one area than another. Minimum requirements Strong technical problem-solving skills, with the ability to develop a working understanding of complex systems through a variety of methods: reading and writing code, studying documentation and partner specifications, running debugging tools, reading logs, querying raw data, and asking precise questions of domain experts Experience building with AI and LLM tooling with established clear outcomes—you don't need experience with any specific framework, but you shou