Revenue Analytics Engineer Data Product Leader

Affirm; Philadelphia (Philadelphia County); On-site; USD 195,000 - 280,000; Full time

Benefits offered by this job

Health care coverage

Flexible Spending Wallets

Time off

ESPP

Job summary

Affirm is a remote-first company revolutionizing credit; we are seeking an experienced analytics engineer to lead scalable data products for our Revenue teams. You will transform ambiguous business problems into durable data solutions covering data modeling, metrics, dashboards, automation and governance across the Revenue data layer.

You will help set the technical direction for Revenue's data layer, establish semantic foundations for AI, and raise data quality and impact across the

Qualifications

• 7+ years in analytics engineering, BI, data engineering or related role.
• Deep expertise in SQL, dbt, data modeling, metrics design, data quality, documentation.
• Experience with modern analytics engineering practices and governance.

Responsibilities

• Lead high-impact data product initiatives for Revenue end-to-end.
• Advance AI initiatives within the Revenue data ecosystem and establish semantic foundations.
• Design and build durable data products powering revenue operations and reporting.
• Set technical direction for Revenue's data layer, including dbt models, semantics, lineage, testing and access controls.
• Mentor analysts and cross-functional partners to raise data product standards.

Skills

SQL

dbt

data modeling

metrics design

data quality

documentation

analytics engineering practices

Tools

Sigma

Looker

Tableau

Snowflake

Databricks

Job description

Affirm is a remote-first company revolutionizing credit; we are seeking an experienced analytics engineer to lead scalable data products for our Revenue teams. You will transform ambiguous business problems into durable data solutions covering data modeling, metrics, dashboards, automation and governance across the Revenue data layer.

You will help set the technical direction for Revenue's data layer, establish semantic foundations for AI, and raise data quality and impact across the