Member of Technical Staff, Reporting & Statements (Backend Engineer)

Responsibilities

• Design and implement RESTful APIs and GraphQL endpoints that serve financial data to internal product teams and external clients.
• Build service integration layer between backend services and the data platform, ensuring reliable data flow and error handling.
• Shape architectural decisions collaboratively for the API and service delivery infrastructure.
• Balance competing requirements of accuracy, performance, and scalability while delivering reporting products on tight client deadlines.
• Collaborate closely with Data Engineers on data contracts and SLAs to ensure seamless integration between the data engine and API layer.
• Partner with Product teams to design APIs that enable self-service access to financial data.
• Champion API design best practices and service reliability standards in partnership with teams across the engineering organization.
• Contribute to technical roadmap planning and help build a supportive, collaborative engineering culture for the new team.

Requirements

• 5-7+ years building backend systems with experience collaborating on designing and shipping production APIs and services that handle complex business logic at scale.
• Service stewardship mindset: responsible for services end-to-end in production environments and understand what it takes to build reliable, observable, maintainable systems.
• Proficient in Go or Python with hands-on experience building RESTful APIs; understand how to design interfaces that balance flexibility with performance.
• Built backend services that interact with databases (PostgreSQL, BigQuery, or similar) and understand query optimization, connection pooling, and data consistency patterns.
• Experience deploying and operating services on cloud platforms (preferably GCP), including containerization, monitoring, and incident response.

Nice-to-Haves

• Built GraphQL APIs and understand the tradeoffs between GraphQL and REST for different use cases.
• Worked in fintech, banking, or similar environments where data accuracy and auditability are critical requirements.
• Worked closely with data engineering teams and understand modern data stack patterns (data warehouses, orchestration, CDC, etc.).