The opportunity
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.
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.
What they're looking for
- Experience: 8+ years of experience in software engineering, data: infrastructure, or data platform engineering, with 2+ years of technical leadership responsibilities.
- Hands-on Leadership: Hands-on experience leading teams to build critical data infrastructure.
- Snowflake / Analytical Warehouses: Hands-on experience with Snowflake or comparable analytical data warehouses, including access control, data masking, query optimization, and cost management.
- Lakehouse and Big Data: Strong experience with Apache Iceberg, Spark, and cloud-native data lake architectures.
- Analytics Engineering: Experience with dbt or equivalent transformation frameworks, including data modeling, testing, documentation, and CI/CD practices.
- Programming Skills: Proficiency in Python, SQL, or JVM-based languages, with a strong emphasis on clean, maintainable, production-quality systems.
- Infrastructure as Code: Familiarity with Terraform or similar automation tools for managing data infrastructure.
- Education: This position requires equivalent practical experience or a Bachelor’s degree in a related field.
