Recruiting Analytics Data Engineer

Anthropic•New York City, NY

7d•Hybrid

About The Position

About the role We are seeking a Recruiting Analytics Data Engineer to join our People Data Solutions team, focusing on building and maintaining the data infrastructure that powers our recruiting analytics capabilities. You'll be the technical foundation for our recruiting analytics team, designing scalable data architectures and implementing robust data models that enable evidence-based decision-making across Anthropic. This role sits at the intersection of data engineering and recruiting analytics - you'll build the technical foundation for insights about recruiting funnels, interviews, and workforce planning while working with a team that's actively experimenting with AI to transform how we understand and support our workforce.

Requirements

Are an expert in BigQuery including optimization and partitioning
Have built dimensional models and understand slowly changing dimensions
Are proficient in SQL, Python, and modern tools like dbt and Fivetran
Have implemented data security and privacy controls in cloud warehouses
Can translate HR concepts into scalable data models
Communicate effectively with both technical and business stakeholders

Nice To Haves

Have 5+ years in data engineering
Familiarity with ATS platforms (Greenhouse, Lever) and their data structures
Experience with building semantic layers for data agents
Experience building data pipelines for survey data and text analytics
Knowledge of graph databases or network analysis libraries
Background in privacy-enhancing technologies or sensitive data handling
Previous experience in high-growth technology companies or AI/ML organizations
Familiarity with workforce planning and predictive analytics use cases

Responsibilities

Refactor and optimize our existing BigQuery tables to create a scalable data foundation that supports and enables AI-driven data insights across the company
Design scalable data architectures and build dimensional models that transform raw HR data into trusted, reusable datasets for self-serve analytics while maintaining performance
Implement data governance including documentation, lineage tracking, quality monitoring, and proactive alerting systems
Ensure appropriate data access controls including row and column-level security for sensitive candidate data
Build and maintain ETL/ELT pipelines using dbt and Google BigQuery to integrate data from our HRIS (Workday), ATS (Greenhouse), and internal tools
Create reliable data flows that handle both real-time needs and batch processing requirements
Design fault-tolerant data pipelines with proper error handling and monitoring to ensure data freshness
Automate data quality checks and validation across all pipelines
Develop semantic layers and comprehensive documentation that make complex recruiting data accessible to non-technical users
Build data products that standardize key metrics like offer accept rate, time to fill, and headcount movement
Partner with data scientists, software engineers, recruiting teams, and various other stakeholders to build scalable data models that serve needs across the company