Revenue Analytics Engineer - AI-Driven Data Products

Affirm; Detroit (MI); On-site; USD 195,000 - 280,000; Full time

Benefits offered by this job

Health benefits

Flexible Spending Wallets

Time off

ESPP

Job summary

Affirm is seeking an experienced analytics engineer to lead the development of scalable data products for the Revenue team. You will turn ambiguous business problems into durable solutions across data modeling, metrics, dashboards, governance, and AI enablement.

You will set technical direction for the Revenue data layer, establish semantic foundations, and raise data quality and impact across the organization.

Qualifications

• 7+ years in analytics engineering or related technical analytics roles.
• Deep SQL, dbt, data modeling, metrics design, data quality, and documentation.
• Experience with BI tools and cloud data warehouses (Snowflake) and Databricks.
• Understanding foundations for reliable AI, including semantic layers, metadata, and access controls.

Responsibilities

• Lead data product initiatives for Revenue from problem definition to rollout.
• Advance AI initiatives by identifying high-value use cases and embedding AI into workflows.
• Design durable data products powering revenue reporting and self-service.
• Set direction for data layer, semantics, lineage, testing, and governance.
• Improve architecture and tooling to scale Revenue Analytics.
• Provide technical leadership and mentorship to analysts and partners.

Skills

SQL

dbt

data modeling

metrics design

data quality

documentation

analytics engineering

BI tools

Snowflake

Databricks

AI enablement

Salesforce familiarity

Tools

Sigma

Looker

Tableau

Job description

Affirm is seeking an experienced analytics engineer to lead the development of scalable data products for the Revenue team. You will turn ambiguous business problems into durable solutions across data modeling, metrics, dashboards, governance, and AI enablement.

You will set technical direction for the Revenue data layer, establish semantic foundations, and raise data quality and impact across the organization.