About the role
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 handling all of Affirm's Data (OLAP and OLTP)
requirements and encompasses the entire range from critical online checkout databases all the way to our Batch Orchestration, Streaming Infrastructure, Event Driven Frameworks, BI and analytics tools and systems. Our mission is to provide trustworthy, intuitive, and cost-efficient solutions for Affirmers to secure, store, analyze, and transform data at exceptional scale. The Data Services organization encompasses the Lake Analytics Platform and Analytics Engineering teams. Our platform powers Affirm's analytical data ecosystem — from the lakehouse and query infrastructure that stores and serves data at scale, to the transformation and modeling layers that make data trustworthy and accessible to the business. We are responsible for Snowflake, FiveTran, Atlan, MonteCarlo, dbt, data governance, privacy controls, and the tooling that enables self-service analytics with an AI focused mindset across the company. What you’ll do As a member of the Data and Storage Services organization, you will collaborate with other teams — including Product, Infrastructure, Lakehouse Infra, Lakehouse Analytics and Analytics Engineering to: Architect and evolve Affirm's lakehouse analytics platform, driving strategy around Snowflake, Apache Iceberg, and Spark to deliver scalable, high-performance analytical infrastructure. Design and implement robust Role-Based Access Control (RBAC) and dynamic data masking policies in Snowflake, ensuring data access is secure, compliant, and auditable across the organization. Lead the technical direction of analytics engineering practices, including data modeling, transformation pipelines (dbt), and data quality frameworks that enable trustworthy, self-service analytics. Drive data governance and privacy engineering initiatives, leveraging tools like Atlan to manage data cataloging, lineage, classification, and policy enforcement. Identify and execute cost optimization strategies across Affirm's analytical compute and storage footprint, including Snowflake warehouse tuning, query optimization, and efficient data lifecycle management. Collaborate with product engineering, data science, and business intelligence teams to understand their data needs and provide continuous guidance on design, architecture, and best practices. Establish and champion best practices for lakehouse operations at scale, including schema evolution, table maintenance, partitioning strategies, and observability. Stay ahead of industry trends in analytical data platforms, data governance, and privacy technologies, and identify opportunities to innovate and improve our data offerings. Mentor engineers across the Lake Analytics Platform and Analytics Engineering teams, providing guidance on emerging technologies, development practices, and fostering a culture of technical excellence. Participate in an on-call rotation and collaborate with other teams such as SRE to resolve production issues. What we look for Architect and Implement: Design, develop, and maintain core components of Affirm's lakehouse analytics platform, with a focus on scalability, governance, and reliability. Snowflake Expertise: Leverage deep knowledge of Snowflake to architect RBAC models, dynamic data masking, warehouse optimization, and multi-cluster compute strategies. Should possess deep understanding of Snowflake internals including query profiling, micro-partitioning, clustering, materialized views, and cost attribution. Analytics Engineering: Drive the technical strategy for data modeling and transformation using dbt, including testing frameworks, documentation standards, and CI/CD for data pipelines. Data Governance & Privacy: Design and operate data governance frameworks using tools like Atlan, including data cataloging, lineage tracking, classification, and automated privacy policy enforcement. Lakehouse Architecture: Tackle the challenges of large-scale analytical data systems, including Apache Iceberg table management, schema evolution, storage optimization, and integration with Spark and Snowflake. Collaboration: Work closely with product managers, software engineers and analysts to translate business
