Staff Software Engineer, Backend (Lake Analytics Platform)

Affirm is reinventing credit to make it more honest and friendly, giving consumers the flexibility to buy now and pay later without any hidden fees or compounding interest. Affirm is reinventing credit to make it more honest and friendly, giving consumers the flexibility to buy now and pay later without any hidden fees or compounding interest. Affirm’s engineering team is building a large-scale, highly available, and global infrastructure that is shared across multiple financial products. Ensuring that our infrastructure is accessible to all engineers is critical to the success of the business. We pride ourselves on our culture across engineering of engaging in thorough technical design review, operational excellence, and capable incident response and analysis. 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. This role will focus on Affirm’s Lakehouse Platform , including Apache Iceberg as a foundational technology for scalable analytical data storage, table management, schema evolution, and interoperability across compute engines such as Spark and Snowflake. What You’ll Do Influence technical strategy: Define and drive the long-term technical roadmap for Affirm’s Lakehouse Platform across Apache Iceberg, Spark, Snowflake, and cloud-native storage, balancing scalability, reliability, governance, performance, and cost. Design and develop: Architect and implement platform capabilities that make analytical data secure, trustworthy, discoverable, and easy to use across Affirm’s engineering, analytics, machine learning, and business teams. Strengthen governance and access controls: Design and operate secure, auditable data access capabilities across Snowflake and the lakehouse platform, including RBAC, dynamic data masking, cataloging, lineage, classification, and privacy policy enforcement. Improve analytics engineering foundations: Partner with Analytics Engineering to evolve data modeling, transformation pipelines, testing frameworks, documentation standards, and data quality practices that enable trustworthy self-service analytics. Operate at scale: Establish best practices for lakehouse operations, including schema evolution, table maintenance, partitioning, compaction, observability, incident response, production support, and readiness for on-call operations. Optimize performance and cost: Identify and execute improvements across analytical compute and storage, including Snowflake warehouse tuning, query optimization, storage layout, lifecycle management, cost attribution, and operational efficiency. Collaborate cross-functionally: Partner with Infrastructure, Lakehouse Analytics, Analytics Engineering, Machine Learning, BI, Product Engineering, and SRE to translate stakeholder needs into durable platform architecture. Innovate: Stay ahead of industry trends in lakehouse architecture, open table formats, analytical compute engines, data governance, privacy engineering, semantic layers, agentic data tools, and AI-ready data infrastructure. Build teams: Mentor engineers, raise technical quality, and foster an inclusive culture of design rigor, operational excellence, and continuous learning. What We Look For Lakehouse Platform Expertise: Proven experience architecting, building, launching, and operating large-scale OLAP systems, lakehouse platforms, or analytical data infrastructure using technologies such as Apache Iceberg, Spark, Snowflake, and cloud-native storage. Snowflake Platform Expertise: Hands-on experience with Snowflake or comparable analytical data warehouses, including RBAC, dynamic data masking, warehouse optimization, query profiling, clustering, and cost management. Data Platform Architecture: Strong understanding of table formats, schema evolution, partitioning, compaction, query performance, data lifecycle management, observability, and cost optimization for analytical systems. Governance and Trust: Experience designing secure, reliable, and governed data platforms, including RBAC/ABAC, data quality, lineage, classification, privacy controls, policy enforcement, and operational compliance. Analytics Engineering Foundations: Experience with dbt or similar transformation frameworks, data modeling best practices, testing, documentation, CI/CD, and data quality practices for analytical pipelines. Agentic Data Tools: Experience building or shaping semantic layers, self-service analytics platforms, internal data applications, or AI-enabled data tools that improve data accessibility and usability. Technical Leadership: Demon