Strategic Overview
Data Engineer, Billing & Payments at Anthropic is a data opportunity centered on the specialist responsibilities and outcomes described in the employer's listing.
The published role emphasizes work such as Partner with Billing Engineering, Finance and Accounting to understand their data and reporting needs, and translate them into technical requirements Design, build and maintain data models and pipelines in dbt that transform raw billing, usage and payments….
The role is listed for San Francisco, CA / New York City, NY with the working arrangement published with the role, giving candidates the practical context needed to assess fit before applying.
Key information at a glance
About This Role
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.
Key Responsibilities & Objectives
- Partner with Billing Engineering, Finance and Accounting to understand their data and reporting needs, and translate them into technical requirements
- Design, build and maintain data models and pipelines in dbt that transform raw billing, usage and payments data into canonical datasets
- Own revenue data accuracy and reconciliation, making sure billing, usage and revenue data tie out across systems
- Define success metrics for billing systems, and build the data models and monitoring to track them
Required Qualifications & Candidate Blueprint
- Experience as a Data Engineer, Analytics Engineer or in a similar role
- Experience with SQL and Python, and building multi-step data pipelines with tools like dbt
- Experience with workflow orchestration tools like Airflow, and version control with GitHub
Salary & Benefits
Published compensation: $320,000 – $405,000 USD
Salary Benchmark
This role’s published compensation is compared with current employer-disclosed salary ranges for the same role in the same currency.
Benchmark uses active employer job postings in our database and excludes this listing. It is not a third-party salary estimate, and currencies are not converted.
Full Job Description
The following is the complete job description supplied by the source. We sanitize unsafe markup and remove external links, but do not shorten or rewrite the employer's source content.
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 supporting Billing & Payments Engineering, you’ll join the Finance Data Engineering team and build the foundation for accurate, scalable billing and revenue data across Anthropic. You’ll work closely with Billing Engineering, Finance and Accounting, as well as Product and other partners, to turn billing and payments data into reliable metrics, reporting and insights. You’ll make sure teams have trustworthy data that scales with our growth, and you’ll lead your own projects to enable self-serve insights that help teams make data-driven decisions.
Key responsibilities
- Partner with Billing Engineering, Finance and Accounting to understand their data and reporting needs, and translate them into technical requirements
- Design, build and maintain data models and pipelines in dbt that transform raw billing, usage and payments data into canonical datasets
- Own revenue data accuracy and reconciliation, making sure billing, usage and revenue data tie out across systems
- Define success metrics for billing systems, and build the data models and monitoring to track them
- Set data integrity standards and SLAs for timely, accurate delivery of billing and revenue data
- Share on-call and data operations responsibilities for Finance Data Engineering pipelines
- Build dashboards and self-serve data products that track core metrics and deliver insights across the company
- Promote best practices for dbt, data modeling and SQL
- Influence the Billing Engineering roadmap from a data systems perspective
- Build a thorough understanding of our data models and data architecture
Minimum qualifications
- Experience as a Data Engineer, Analytics Engineer or in a similar role
- Experience with SQL and Python, and building multi-step data pipelines with tools like dbt
- Experience with workflow orchestration tools like Airflow, and version control with GitHub
- Familiarity with billing concepts such as invoicing, payments, credits, refunds and subscriptions, and experience working with data from billing systems
- Experience building dashboards and reporting in tools like Hex for multiple cross-functional teams
- Ability to prioritize and deliver practical solutions quickly
- Willingness to solve problems end to end, including work beyond the original scope
- Ability to define next steps and keep work moving when requirements are unclear
- Interest in Anthropic’s mission of building safe and beneficial AI
Preferred qualifications
- 5+ years of experience as a Data Engineer, Analytics Engineer or in a similar role
- Experience partnering with billing, finance or accounting teams on revenue metrics and reconciliation
- Experience building an analytics or data engineering function from an early stage
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.
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.
Editor’s Market Perspective (Our Custom Analysis)
Why This Position Matters
This data opening is a useful opportunity for candidates whose experience matches the role-specific capabilities named in the listing. Its strongest career signal is the scope of the work itself — particularly Partner with Billing Engineering, Finance and Accounting to understand their data and reporting needs, and translate them into technical requirements — rather than a generic title alone.
Resume & Application Strategy
For the application, mirror the employer's language where it truthfully matches your background: foreground the role-specific capabilities named in the listing, quantify outcomes from comparable work, and place the most relevant experience near the top of the resume. If the listing specifies tools, platforms, leadership scope, or domain experience, use those exact terms in context instead of presenting an unrelated keyword list.
We organize source-provided information into this independent job-seeker overview. Compensation is shown only when the source provides it; the official listing remains the authority for current requirements and application instructions.