As a Data Engineer on the Data Science & Analytics team, you will build the data foundation for Anthropic's quote-to-cash lifecycle: the path a deal takes from opportunity through revenue. You will design the canonical data models so that Sales, Deal Desk, Order Management, Revenue Operations and Finance work from one governed, auditable definition of what was sold, on what terms, and where each deal stands. You will partner closely with DS&A and with GTM and Finance systems teams who own Salesforce, CPQ and billing to make quote-to-cash data reliable, well-modeled and self-serve as our business scales. Responsibilities • Understand the data needs of Deal Desk, Order Management, Revenue Operations, Finance and Sales systems teams, and translate them into technical requirements • Design, build and own data models that transform raw Salesforce, CPQ, and billing data into canonical datasets • Establish high data integrity standards and SLAs to ensure timely, accurate delivery of data • Partner with Salesforce, CPQ and billing engineers on upstream schema changes, new fields and ingestion so the warehouse faithfully mirrors the systems of record • Build foundational data products, dashboards and tools to enable self-serve analytics to scale across GTM teams • Influence stakeholder roadmaps from a data perspective, and become the expert on Anthropic's GTM data models and architecture You might be a good fit if you have • 5+ years of experience as a Data Engineer, Analytics Engineer or in a similar Data Science & Analytics role, ideally partnering with GTM, Revenue Operations or Finance teams. • A passion for the company's mission of building helpful, honest, and harmless AI. • Hands-on experience modeling Salesforce data and at least one adjacent quote-to-cash system: CPQ, contract lifecycle management, billing and invoicing, or ERP. • Expertise in building multi-step ETL jobs, building robust data models through tooling like dbt; proficiency with workflow management platforms like Airflow and version control management tools through GitHub. • Expertise in SQL and Python to transform data into accurate, clean data models. • Experience building data reporting and dashboarding in visualization tools like Hex to serve multiple cross-functional teams. • A bias for action and urgency, not letting perfect be the enemy of the effective. • A "full-stack mindset", not hesitating to do what it takes to solve a problem end-to-end, even if it requires going outside the original job description. • Experience building an Analytics Data Engineering (or similar) function at start-ups. • A strong disposition to thrive in ambiguity, taking initiative to create clarity and forward progress. The annual compensation range for this role is listed below. For sales roles, the range provided is the role's On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $320,000-$405,000 USD Logistics Minimum education: Bachelor's degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.