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Salary: USD 195,000 - 280,000 / annual
Affirm is seeking an experienced Staff Analytics Engineer to lead the Revenue Analytics team's data products, reporting infrastructure, and analytical systems. This is a high-impact individual contributor role focused on building scalable, trusted data solutions for Affirm's revenue organization.
You will own the end-to-end development of data products—from ambiguous problem definition through design, implementation, rollout, and adoption. Key responsibilities include:
• Leading high-impact data product initiatives for Revenue teams, managing complex projects independently from scope definition through delivery.
• Advancing AI initiatives within the Revenue data ecosystem by identifying use cases, integrating AI into analytics workflows, and establishing semantic, metadata, and quality foundations.
• Designing and building durable data products powering revenue operations, field/executive reporting, merchant reporting, and analyst self-service.
• Setting technical direction for Revenue's data layer, including dbt models, metrics, semantic structures, documentation, lineage, testing, and governance.
• Identifying opportunities to simplify, automate, and scale Revenue Analytics through improved architecture, tooling, and enablement.
• Providing technical leadership and mentorship to analysts and cross-functional partners, raising standards for data product design and maintenance.
Required qualifications: 7+ years in analytics engineering, business intelligence, data engineering, or related technical analytics roles. Deep expertise in SQL, dbt, data modeling, metrics design, and modern analytics engineering practices. Strong knowledge of BI tools (Sigma, Looker, Tableau), cloud data warehouses (Snowflake), and modern data platforms (Databricks). Understanding of AI foundations including semantic layers, metadata, evaluations, and data quality. Demonstrated ability to independently lead complex, ambiguous projects with strong stakeholder alignment skills. Experience designing scalable data products for multiple audiences. Familiarity with Salesforce and commercial/GTM/revenue operations is preferred.
