Recruiting Analytics Data Engineer at Anthropic

Hybrid - New York City, NY; San Francisco, CA; Seattle, WA

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The Recruiting Analytics Data Engineer role joins Anthropic’s People Data Solutions team to build and maintain the data infrastructure powering recruiting analytics. Responsibilities include optimizing BigQuery tables, designing scalable data architectures and dimensional models, implementing robust data governance, building ETL/ELT pipelines with dbt and Google BigQuery, developing semantic layers, and collaborating with data scientists, engineers, and recruiting stakeholders to enable evidence‑based decision making across the company.

Salary

USD 285,000 - 380,000

Requirements

Skills

  • Expert in BigQuery including optimization and partitioning
  • Built dimensional models and understand slowly changing dimensions
  • Proficient in SQL, Python, and modern tools like dbt and Fivetran
  • Implemented data security and privacy controls in cloud warehouses
  • Translate HR concepts into scalable data models
  • Communicate effectively with both technical and business stakeholders
  • 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
  • Bachelor’s degree or an equivalent combination of education, training, and/or experience

Responsibilities

  • Refactor and optimize existing BigQuery tables to create a scalable data foundation
  • Design scalable data architectures and build dimensional models that transform raw HR data
  • 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 HRIS (Workday), ATS (Greenhouse), and internal tools
  • Create reliable data flows that handle 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 such as offer accept rate, time to fill, and headcount movement
  • Partner with data scientists, software engineers, recruiting teams, and other stakeholders to build scalable data models serving company needs

Technologies

Google BigQuerySQLPythondbtFivetranWorkdayGreenhouseATSGraph databases

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