Job description

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Role 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. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Requirements

Must-haves

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.
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.
How we're different
Come work with us!

Nice-to-haves

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Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aisqlpython

Platform: greenhouse
Effort: quick

Benefits

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Parental Leave