Anthropic-posted about 1 month ago
Full-time • Mid Level
Hybrid • San Francisco, CA
1,001-5,000 employees
Publishing Industries

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. We are seeking a People Analytics Data Engineer to join our People Data Solutions team, focusing on building and maintaining the data infrastructure that powers our people analytics capabilities. You'll be the technical foundation for our people 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 people analytics - you'll build the technical foundation for insights about engagement, performance, and workforce planning while working with a team that's actively experimenting with AI to transform how we understand and support our workforce. You'll report to the Head of People Data Solutions.

  • Refactor and optimize our existing BigQuery tables to create a scalable data foundation that supports traditional BI tools (like Looker) and enables AI-driven 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 employee data
  • Build and maintain ETL/ELT pipelines using dbt and Google BigQuery to integrate data from our HRIS (Rippling/Workday), ATS (Greenhouse), survey platforms (Qualtrics), and collaboration 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 HR data accessible to non-technical users
  • Build data products that standardize key metrics like attrition rates, engagement scores, and employee mobility
  • Create specialized data structures for organizational network analysis (ONA) including team dynamics and reporting relationships
  • Partner with people analytics data scientists, recruiting teams, and various other stakeholders to build scalable data models that serve needs across the company
  • Have 5+ years in data engineering
  • 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
  • Understand organizational network analysis and graph data concepts
  • Communicate effectively with both technical and business stakeholders
  • Experience with HRIS data models and integrations (i.e. Rippling, Workday, Lattice, Qualtrics)
  • Familiarity with ATS platforms (Greenhouse, Lever) and their data structures
  • 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
  • competitive compensation and benefits
  • optional equity donation matching
  • generous vacation and parental leave
  • flexible working hours
  • a lovely office space in which to collaborate with colleagues
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