Anthropic
Recruiting Analytics Data Engineer
About this role
Anthropic seeks a Recruiting Analytics Data Engineer to design and maintain scalable data infrastructure supporting recruiting analytics. You'll build data architectures, ETL pipelines, and semantic layers that transform HR data into actionable insights for workforce planning and decision-making.
What you'll do
- Refactor and optimize BigQuery tables to create scalable data foundations for AI-driven insights
- Design dimensional data models and implement data governance with documentation, lineage tracking, and quality monitoring
- Build and maintain ETL/ELT pipelines using dbt, BigQuery, and integrations with Workday and Greenhouse
- Implement data security controls including row and column-level access restrictions for sensitive candidate data
- Develop semantic layers and standardized metrics like offer accept rate and time to fill for self-serve analytics
- Partner with data scientists, engineers, and recruiting teams to build scalable models serving company-wide needs
What they're looking for
- BigQuery optimization and partitioning
- Dimensional modeling and slowly changing dimensions
- SQL and Python proficiency
- dbt and Fivetran experience
- Data security and privacy controls in cloud warehouses
- ETL/ELT pipeline development
- HR data concepts and recruiting domain knowledge
- Stakeholder communication (technical and business)
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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 how you've optimized BigQuery performance for large-scale HR datasets—what partitioning and clustering strategies did you use?
- Describe your experience implementing row and column-level security in a cloud data warehouse for sensitive candidate or employee information.