Senior Staff Software Engineer, Backend (Data and Storage Services)
About the role
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.
Responsibilities
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.
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.
Drive the technical strategy for data modeling and transformation using dbt, including testing frameworks, documentation standards, and CI/CD for data pipelines.
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.
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.
Work closely 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.
Qualifications
Experience: 10+ years of experience in software engineering or data engineering, with a proven track record of delivering complex data platform solutions that improve accessibility, performance, and governance of analytics infrastructure.
Snowflake Expertise: 6+ years of hands-on experience with Snowflake or comparable analytical data warehouses, including RBAC design, data masking, query optimization, and cost management.
Lakehouse & Big Data: Strong experience with Apache Iceberg, Spark, and cloud-native data lake architectures on AWS (S3, EKS).
Analytics Engineering: Experience with dbt or equivalent transformation frameworks, including data modeling best practices, testing, and CI/CD for data pipelines.
Problem Solving: Exceptional problem-solving and analytical skills, with the ability to identify and resolve complex technical challenges and establish long-lasting solutions and processes.
Programming Skills: Proficiency in Python and SQL, with a strong emphasis on clean, maintainable code. Experience with Kotlin or Go is a plus.
Leadership: Demonstrated leadership and mentorship skills, with the ability to inspire and guide others.