Staff Analytics Analyst, Full Stack (Revenue)

Affirm is reinventing credit to make it more honest and friendly. We're seeking a Staff Analytics Analyst to lead full-stack revenue data initiatives, from problem definition through scalable implementation and adoption across Affirm's Revenue organization.

About the Role

You will own high-impact data product development for the Revenue Analytics team, translating complex business and technical challenges into durable, scalable solutions. This is a technical leadership role focused on data modeling, metrics architecture, semantic foundations, AI enablement, and governance. You'll set technical direction for Revenue's data layer while mentoring analysts and cross-functional partners.

Responsibilities

• Lead end-to-end 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, establishing standards for data product design, build, and maintenance.

Requirements

• 7+ years in analytics engineering, business intelligence, data engineering, data product development, or related technical analytics roles.
• 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.
• Strong working knowledge of cloud data warehouses such as Snowflake and modern data platforms such as Databricks.
• Demonstrated understanding of AI reliability foundations, including semantic layers, metadata, evaluations, documentation, lineage, access controls, and data quality.

Location & Remote Policy

Remote, US based.