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
Lead high-impact data product initiatives for Revenue, from ambiguous problem: definition through technical design, implementation, rollout, and adoption.
Advance AI initiatives within the Revenue data ecosystem by identifying: high-value use cases, integrating AI into analytics engineering workflows, and establishing the semantic, metadata, context, quality, and evaluation foundations required for AI.
Design and build durable, well-tested data products that power revenue: operations, field and executive reporting, external merchant reporting, and analyst self-service.
Set technical direction for Revenue’s data layer across dbt models, metrics,: semantic structures, documentation, lineage, testing, governance, and access controls.
Identify opportunities to simplify, automate, and scale Revenue Analytics: through improved data architecture, tooling, governance, and enablement.
Provide technical leadership and mentorship to analysts and cross-functional: partners, raising the bar for how data products are designed, built, and maintained.
What they're looking for
- + years of experience in analytics engineering, business intelligence, data: engineering, data product development, or a related technical analytics role.
- Deep expertise in SQL, dbt, data modeling, metrics design, data quality,: documentation, and modern analytics engineering practices.
- Strong working knowledge of BI tools such as Sigma, Looker, or Tableau, cloud: data warehouses such as Snowflake, and modern data platforms such as Databricks.
- Strong understanding of the foundations required for reliable AI—including: semantic layers, metadata, evaluations, documentation, lineage, access controls, and data quality—and experience applying AI tools within analytics engineering workflows.
