Remote Revenue Analytics Engineer - Full Stack

Affirm; New York (NY); Remote; USD 220,000 - 280,000; Full time

Benefits offered by this job

Health care coverage

Flexible Spending Wallets

Time off

Employee stock purchase plan

Job summary

Affirm is seeking an experienced analytics engineer to lead scalable data products for the Revenue organization. You will turn ambiguous business problems into scalable data models, metrics, dashboards, and governance foundations, enabling AI-enabled analytics and trusted data across revenue teams.

You will guide the technical direction of the Revenue data layer, establish semantic and metadata foundations, and raise data quality and impact across the organization.

Qualifications

• 7+ years in analytics engineering, BI, data engineering, or related analytics role.
• Deep expertise in SQL, dbt, data modeling, metrics design, data quality, and documentation.
• Strong knowledge of BI tools and modern data platforms.
• Experience applying AI tools within analytics engineering workflows.

Responsibilities

• Lead data product initiatives for Revenue from problem definition to rollout.
• Advance AI initiatives by integrating AI into analytics workflows and establishing semantic, metadata, and quality foundations.
• Design and build durable data products powering revenue reporting and self-service analytics.
• Set technical direction for Revenue's data layer including dbt models, metrics, semantics, documentation, lineage, testing, governance and access controls.
• Identify opportunities to simplify, automate, and scale Revenue Analytics through improved architecture and tooling.
• Provide technical leadership and mentorship to analysts and cross-functional partners.

Skills

SQL

dbt

data modeling

metrics design

data quality

documentation

analytics engineering

Tools

Sigma

Looker

Tableau

Snowflake

Databricks

Job description

Affirm is seeking an experienced analytics engineer to lead scalable data products for the Revenue organization. You will turn ambiguous business problems into scalable data models, metrics, dashboards, and governance foundations, enabling AI-enabled analytics and trusted data across revenue teams.

You will guide the technical direction of the Revenue data layer, establish semantic and metadata foundations, and raise data quality and impact across the organization.