Senior Software Engineer, Backend (Lake Analytics Platform)

<div class="content-intro"><p>At Affirm, we exist for the moments that matter—giving people a clear, predictable way to pay over time, with no hidden fees, no surprises, and no tradeoffs on what matters most.</p></div><p>The Data and Storage Services team is responsible for Affirm's data infrastructure across OLTP and OLAP systems, spanning critical online checkout databases, batch orchestration, streaming infrastructure, event-driven frameworks, BI, analytics tooling, large-scale data platforms, and agentic data tools such as semantic layers and internal platform data applications. Our mission is to provide trustworthy, intuitive, and cost-efficient solutions for Affirmers to secure, store, analyze, and transform data at exceptional scale.</p> <p>This role focuses on the platform and applications layer of Affirm's data infrastructure — building, operating, and extending systems that enable teams across Affirm to work with data at scale. You will be a core contributor to platform reliability, self-service capabilities, and the roadmap toward agentic data tooling and semantic layer infrastructure.</p> <h2><strong>What You'll Do</strong></h2> <ul> <li><strong>Build and operate core platform capabilities:</strong> Design and implement platform features that enable engineers across Affirm to build, deploy, and operate data applications at scale — covering automated provisioning, deploy pipelines, access control, service lifecycle management, and reliability tooling.</li> <li><strong>Develop integrations:</strong> Build and maintain integrations between the platform and internal systems — CI/CD pipelines, identity and secrets management, external APIs, and event-driven hooks — so that application teams have secure, reliable, and self-service access to the tools they need.</li> <li><strong>Strengthen data access and governance:</strong> Design and operate secure data access patterns across the platform's analytical infrastructure, including RBAC, managed-access schemas, dynamic data masking, secure views, and cross-database grants that make platform data trustworthy without creating operational bottlenecks.</li> <li><strong>Automate toil and unlock self-service:</strong> Identify recurring support patterns — access provisioning, data pipeline failures, service health management, secrets management — and build the automation and tooling to make them self-service for platform users, eliminating manual admin work.</li> <li><strong>Improve observability and incident response:</strong> Build telemetry, alerting, and log infrastructure so platform teams and application owners can diagnose and resolve issues independently. Establish runbooks and operational patterns that reduce blast radius and recovery time.</li> <li><strong>S