Analytics Engineer II, Full Stack (Revenue Analytics)

At Affirm, we exist for the moments that matter—giving people a clear, predictable way to pay over time, with no hidden fees, no surprises, and no tradeoffs on what matters most.

We’re looking for a curious, driven professional to join our Revenue Analytics team. This builds and owns the data products, reporting infrastructure, semantic foundations, and analytical systems that power Affirm’s Revenue organization. As a Senior Analyst, Revenue Analytics, you’ll build scalable data products that power day-to-day decision-making - owning end-to-end work across data modeling, metric definitions, dashboards, automation, and enablement. You’ll also help strengthen our semantic layer and data governance, laying the foundation for reliable AI. The ideal candidate combines strong technical and analytical skills with the ability to turn ambiguous business questions into durable, well-tested data infrastructure.

What you'll do

-

Develop dbt data models, dashboards, metrics, and automation processes for the revenue field team and revenue analysts

-

Build and maintain critical reporting data models that power external merchant reporting

-

Build the semantic, metadata, and context layers that allow AI systems to accurately understand Revenue data, metrics, and business definitions

-

Partner with Business Systems, engineering, and business stakeholders to translate requirements into durable, well-tested data products

-

Contribute to the team’s best practices in version control, code review, documentation, and release hygiene (GitHub-based workflows)

-

Develop processes, governance, and foundations to scale the impact of analytics within Revenue.

What we look for

-

3+ years of work experience in an analytics engineering or business intelligence role

-

Strong working knowledge of SQL, dbt, Python, data modeling, and data visualization

-

Hands-on experience with BI tools (Sigma/Looker/Tableau), Databricks, and cloud data warehouses (Snowflake)

-

Understanding of the data foundations required for reliable AI, including semantic layers, metadata, evals, metric definitions, documentation, and data quality

-

Demonstrated experience integrating AI tools into day-to-day analytics engineering workflows to improve development speed, quality, and scalability

-

Familiarity with Salesforce and experience supporting commercial areas of the business

-

Ability to identify user needs and translate them into robust, scalable data products

-

Ability to start with an ambiguous problem, deconstruct it into tangible steps, and work toward an impactf