SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Salary: EUR 63,000 - 99,000 / annual
Affirm is reinventing credit to make it more honest and friendly, offering consumers flexible buy-now-pay-later options without hidden fees or compounding interest. The Revenue Analytics team builds and owns the data products, reporting infrastructure, semantic foundations, and analytical systems that power Affirm's Revenue organization.
As an Analyst II in Revenue Analytics, you will build scalable data products that enable day-to-day decision-making across the revenue organization. You'll own end-to-end work spanning data modeling, metric definitions, dashboards, automation, and stakeholder enablement. A key focus is strengthening the semantic layer and data governance to establish reliable foundations for AI systems.
Key responsibilities include:
- Developing dbt data models, dashboards, metrics, and automation processes for revenue field teams and analysts
- Building and maintaining critical reporting data models that power external merchant reporting
- Constructing semantic, metadata, and context layers that enable AI systems to accurately understand revenue data, metrics, and business definitions
- Partnering with Business Systems, engineering, and business stakeholders to translate requirements into durable, well-tested data products
- Contributing to team best practices in version control, code review, documentation, and release hygiene using GitHub-based workflows
- Developing processes, governance, and foundations to scale analytics impact within Revenue
You should have 3+ years of experience in analytics engineering or business intelligence roles, with strong proficiency in SQL, dbt, Python, data modeling, and data visualization. Hands-on experience with BI tools (Sigma, Looker, or Tableau), Databricks, and cloud data warehouses (Snowflake) is required. Understanding of data foundations for reliable AI—including semantic layers, metadata, evals, metric definitions, documentation, and data quality—is essential. Experience integrating AI tools into analytics engineering workflows and familiarity with Salesforce supporting commercial functions is valued. You should excel at identifying user needs, translating ambiguous problems into tangible steps, and communicating findings clearly to both technical and non-technical audiences.
