Staff / Principal

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. 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.

• 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.
• 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.
• 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.
• Partner with Analytics Engineering to evolve data modeling, transformation pipelines, testing frameworks, documentation standards, and data quality practices that enable trustworthy self-service analytics.
• Establish best practices for lakehouse operations, including schema evolution, table maintenance, partitioning, compaction, observability, incident response, production support, and readiness for on-call operations.
• Identify and execute improvements across analytical compute and storage, including Snowflake warehouse tuning, query optimization, storage layout, lifecycle management, cost attribution, and operational efficiency.
• Partner with Infrastructure, Lakehouse Analytics, Analytics Engineering, Machine Learning, BI, Product Engineering, and SRE to translate stakeholder needs into durable platform architecture.
• 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.
• Mentor engineers, raise technical quality, and foster an inclusive culture of design rigor, operational excellence, and continuous learning.

• 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.
• Hands-on experience with Snowflake or comparable analytical data warehouses, including RBAC, dynamic data masking, warehouse optimization, query profiling, clustering, and cost management.
• Strong understanding of table formats, schema evolution, partitioning, compaction, query performance, data lifecycle management, observability, and cost optimization for analytical systems.
• Experience designing secure, reliable, and governed data platforms, including RBAC/ABAC, data quality, lineage, classification, privacy controls, policy enforcement, and operational compliance.
• Experience with dbt or similar transformation frameworks, data modeling best practices, testing, documentation, CI/CD, and data quality practices for analytical pipelines.
• Experience building or shaping semantic layers, self-service analytics platforms, internal data applications, or AI-enabled data tools that improve data accessibility and usability.
• Demonstrated ability to set technical direction, lead ambiguous platform initiatives, mentor engineers, and influence roadmaps across teams while staying close to implementation details.
• Strong ability to partner with engineering, analytics, machine learning, BI, product, and infrastructure teams to translate business needs into durable technical solutions.
• Excellent communication skills, with the ability to clearly articulate technical concepts, tradeoffs, and recommendations to technical and non-technical stakeholders.

• 8+ years of experience in software engineering, data infrastructure, or data platform engineering, with 2+ years of technical leadership responsibilities.
• Hands-on experience leading teams to build critical data infrastructure.
• Hands-on experience with Snowflake or comparable analytical data warehouses, including access control, data masking, query optimization, and cost management.
• Strong experience with Apache Iceberg, Spark, and cloud-native data lake architectures.
• Experience with dbt or equivalent transformation frameworks, including data modeling, testing, documentation, and CI/CD practices.
• Proficiency in Python, SQL, or JVM-based languages, with a strong emphasis on clean, maintainable, production-quality systems.
• Familiarity with Terraform or similar automation tools for managing data infrastructure.
• This position requires equivalent practical experience or a Bachelor’s degree in a related field.

CAN base pay range per year: 181,000 - 241,000. Employees new to Affirm typically come in at the start of the pay range. Base pay is part of a total compensation package that may include monthly stipends for health, wellness and tech spending, and benefits (including 100% subsidized medical coverage, dental and vision for you and your dependents). In addition, employees may be eligible for equity rewards offered by Affirm Holdings, Inc. (parent company).