Role Description The Financial Platform Eng team is part of Block's platform engineering org, with a mandate to run the foundational financial technology infrastructure supporting all our brands. Our charter includes building scalable, reliable, and forward-looking financial infrastructure, from revenue streams to reconciliations to GL to street reporting. This team powers key business functions spanning the CFO org and aids at strategic decision-making. As a Software Engineer, you'll design and build the backend services, integrations, and workflow engines that keep the foundational financial operations running. Often you will focus on transforming complex financial requirements into scalable technology solutions — with a specific emphasis on AI enablement. We are an AI-driven engineering team — we use agentic coding tools across our entire development lifecycle, from design exploration through implementation and review, and we expect every engineer to do the same. Success in this role depends on strong collaboration with cross-functional partners including Accounting, FP&A, Tax, Procurement, Product, TPMs, Engineering, and Data teams. The ability to operate across these verticals while maintaining a horizontal architectural perspective is key to our impact. You Will - Build financial operations and reporting infrastructure — Design and implement backend services for Accounting, FP&A, Tax, Procurement while keeping the company compliant. - Lead a platform migration — Architect the migration of business-critical workflows from legacy vendor platforms to Block's internal systems, ensuring zero disruption to agents and customers during cutover. - Design workflow and automation systems — Build configurable workflow engines, rules-based routing, and event-driven automations that reduce manual effort and scale across multiple business lines. - Own integrations end-to-end — Design, build, and maintain integrations between internal platforms and external systems (middleware, workforce management, Oracle fusion, Salesforce etc) using APIs, event streams, and messaging patterns. - Partner across the business — Work directly with operations, compliance, and product stakeholders to translate complex financial and regulatory requirements into well-architected technical solutions. - Ensure compliance and data integrity — Build systems with audit trails, access controls, and data governance baked in — not bolted on — to meet regulatory requirements across multiple jurisdictions. - Lead technical discussions — Drive architecture decisions, contribute to design reviews, and mentor engineers; raise the bar on code quality, testing, and operational reliability. - Direct AI agents across the full development lifecycle — from prototyping and implementation to debugging and code review — operating as the architect and decision-maker while AI handles the building. - Expand what's possible with AI tooling — identify opportunities to automate repetitive engineering work, improve code quality through AI-driven review, and share effective patterns with your team. Qualifications - Embrace an AI-first mentality. Leverage AI to augment your knowledge and capability in navigating development in complex systems with confidence. - Familiar with agentic engineering. - 6+ years working on complex systems and delivering quality software, with clear expertise developed in one or more technical areas. - Strong fundamentals in distributed systems design — you've built services that handle high throughput, manage state across systems, and degrade gracefully. - Experience with workflow or state machine systems — you've designed systems where entities move through complex lifecycles with branching logic, SLAs, and human-in-the-loop steps. - Proficiency in building and maintaining APIs and integrations — REST, gRPC, event-driven architectures, and data synchronization across multiple platforms. - Experience working on financially sensitive or regulated systems — you understand why idempotency, audit trails, data retention, and access controls matter and you build for them by default. - Strong collaboration skills and the ability to work effectively with non-technical stakeholders — translating ambiguous business requirements from operations, compliance, and risk teams into well-scoped engineering work. - A growth mindset and comfort navigating open-ended problems where the "right" answer depends on business context that's still evolving. - Contributed to the growth of fellow engineers through technical leadership, mentoring, and setting engineering standards. - Demonstrated fluency with AI-assisted development tools (e.g., Claude Code, Cursor, GitHub Copilot, or similar agentic coding tools) in real production work — not just familiarity, but regular, effective use. - You stay current on the rapidly evolving landscape of AI tooling and actively experiment with new capabilities as they emerge. - Curiosity that pushes you to find flaws in complex systems and take whatever steps are necessary to fix them. - Resilience to persevere in complex situations across multiple axes: e.g., scale, uncertainty, and interconnectedness. - Self-directed and comfortable with ambiguity, with a willingness to publish your reasoning and be wrong publicly—which is essential for thriving in our transparent DRI/IC model. Nice to Have - Experience with platform migrations — moving business-critical systems between platforms with zero customer impact and phased rollout strategies. - Background in financial services domains — accounting, revenue forecasting, treasury operations, tax operations. - Experience building multi-tenant or multi-BU platforms — systems that serve different business lines with shared infrastructure but configurable behavior. - Experience with operational analytics — building data pipelines or reporting systems that give operations leaders real-time visibility into performance. Technologies We Use and Teach - AI-Driven Development: Claude Code, Goose (Block's open-source AI agent), Cursor, GitHub Copilot, Anthropic's Model Context Protocol (MCP). - Languages: Kotlin, Java, Python, JavaScript. - Protocols: HTTP, JSON, gRPC, Protocol Buffers. - Data: DynamoDB, MySQL, Kafka, Snowflake. - Infrastructure: AWS, Kubernetes. - Observability: DataDog, Prometheus. - Feature Management: LaunchDarkly. Benefits - Remote work. - Medical insurance. - Flexible time off. - Retirement savings plans. - Modern family planning.