GTM Data Engineer for Salesforce & Quote-to-Cash

Anthropic; New York (NY); Hybrid; USD 320,000 - 405,000; Full time

Job summary

Anthropic is seeking a Data Engineer to build the data foundation for its quote-to-cash lifecycle. You will design canonical data models so Sales, Deal Desk, Order Management, Revenue Operations and Finance work from a single auditable definition of what was sold and on what terms.

You will transform raw Salesforce, CPQ, and billing data into robust data models, build data products and dashboards, and partner with finance and GTM teams to ensure reliable, self-serve analytics as Anthropic scales.

Qualifications

• 5+ years of experience as a Data Engineer, Analytics Engineer or in a similar Data Science & Analytics role.
• Hands-on experience modeling Salesforce data and adjacent quote-to-cash systems (CPQ, billing, ERP).
• Expertise in SQL and Python to transform data into accurate, clean models.
• Experience building data reporting and dashboards for cross-functional teams.
• Strong ability to work with GTM, Revenue Operations or Finance stakeholders.

Responsibilities

• Design, build and own canonical data models from Salesforce, CPQ and billing data.
• Create data products, dashboards and tools for self-serve analytics across GTM teams.
• Establish data integrity SLAs and ensure timely delivery of data.
• Collaborate with Salesforce, CPQ and billing engineers on upstream schema changes.
• Influence stakeholder roadmaps with robust GTM data models and architecture.

Skills

SQL

Python

Data Modeling

ETL

dbt

Airflow

GitHub

Salesforce

CPQ

Billing

Analytics

Communication

Education

Bachelor's degree

Tools

dbt

Airflow

GitHub

Job description

Anthropic is seeking a Data Engineer to build the data foundation for its quote-to-cash lifecycle. You will design canonical data models so Sales, Deal Desk, Order Management, Revenue Operations and Finance work from a single auditable definition of what was sold and on what terms.

You will transform raw Salesforce, CPQ, and billing data into robust data models, build data products and dashboards, and partner with finance and GTM teams to ensure reliable, self-serve analytics as Anthropic scales.