Anthropic
Accounting Analytics & BI Engineer
About this role
Build and maintain the data foundation for Anthropic's Accounting organization, designing data models, orchestration pipelines, and self-serve dashboards that enable reliable financial reporting and support AI-powered accounting automation at a fast-growing AI safety company.
What you'll do
- Design and maintain data models and orchestration pipelines using dbt and Airflow for analytics and financial workflows
- Develop dashboards and data applications giving accounting stakeholders self-serve data access
- Translate business questions into metrics and analysis, providing clear recommendations to finance teams
- Build analytics supporting financial reporting, close processes, reconciliations, and scenario analysis
- Partner on AI-powered accounting automation initiatives to streamline workflows and reduce manual effort
- Champion data quality, testing, and documentation across the data stack
What they're looking for
- SQL and Python (or object-oriented programming language)
- Data modeling and orchestration tools (dbt, Airflow)
- Data visualization and BI tools (Looker, Tableau, Hex, Streamlit)
- Self-serve analytics design for non-technical users
- Cloud computing platforms (AWS, GCP) and cost structures
- Financial modeling and scenario analysis
- AI/ML integration in analytical processes
- Data application development
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Anthropic
Anthropic builds Claude, an AI assistant, and is hiring for engineering roles across infrastructure, data systems, and security that support both AI research operations and the company's internal technology needs. The company seeks infrastructure engineers, systems integrators, data scientists, and security specialists to build production-scale systems for training data pipelines, financial operations, developer productivity measurement, research infrastructure, and server firmware security.
- Website
- anthropic.com
Likely interview questions
- Walk us through a time you designed a self-serve analytics dashboard or data application; how did you balance stakeholder needs with technical constraints?
- Describe your experience building data pipelines with dbt and Airflow—what was the most complex orchestration challenge you solved?