Revenue Analytics Engineer - Full Stack (Remote)

Affirm; Winnipeg; On-site; CAD 173,000 - 233,000; Full time

Benefits offered by this job

Equity

Health coverage

Tech stipend

Flexible Spending Wallets

Time off

Employee Stock Purchase Plan (ESPP)

Job summary

Affirm is seeking an experienced analytics engineer to join the Revenue Analytics team. You will lead scalable data products for revenue teams, turning ambiguous problems into durable solutions across data modeling, metrics, dashboards, governance, and AI enablement.

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

Qualifications

• 7+ years of analytics engineering/data product development experience.
• Deep expertise in SQL, dbt, data modeling and metrics design.
• Proficiency with BI tools and cloud data warehouses.
• Experience enabling AI within analytics workflows and governance.

Responsibilities

• Lead high-impact data product initiatives for Revenue from scope through rollout.
• Advance AI initiatives within the Revenue data ecosystem and establish semantic foundations.
• Design and build durable data products powering revenue reporting and self-service.
• Set technical direction for Revenue's data layer including governance and access controls.
• Identify opportunities to automate and scale analytics through improved data architecture.
• Provide technical leadership and mentorship to analysts and cross-functional partners.

Skills

SQL

Data Modeling

Metrics Design

Data Quality

Documentation

Analytics Engineering Practices

AI Enablement

Tools

dbt

Snowflake

Databricks

Looker

Sigma

Tableau

Job description

Affirm is seeking an experienced analytics engineer to join the Revenue Analytics team. You will lead scalable data products for revenue teams, turning ambiguous problems into durable solutions across data modeling, metrics, dashboards, governance, and AI enablement.

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