Recruiting Analytics Data Engineer

Anthropic · New York City, NY; San Francisco, CA | New York City, NY | Seattle, WA; Seattle, WAmid

En corto

▸Ingeniero de datos que construye infraestructura de datos para analytics de reclutamiento en BigQuery.
▸Diseña modelos escalables, pipelines ETL/ELT con dbt y Fivetran, y garantiza seguridad de datos sensibles.
▸Destaca por su enfoque en datos de reclutamiento con aplicaciones de IA y modelos dimensionales para decisiones basadas en evidencia.

Fluent English required

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¿Qué piden?

✓Experto en BigQuery con optimización y particionamiento
✓Experiencia en modelos dimensionales y dimensiones lentas
✓Dominio de SQL, Python, dbt y Fivetran
✓Implementación de controles de seguridad y privacidad en almacenes en la nube
✓Capacidad para traducir conceptos de RH en modelos de datos escalables
✓Comunicación efectiva con equipos técnicos y no técnicos

¿No cumplís todo? Es lo normal — tu dossier gratis te dice qué gaps tenés y cómo cubrirlos en la entrevista.

BigQuerydbtFivetranWorkdayGreenhouseSQLPythonETL/ELTdata governancerow-level security

¿A quién escribirle en Anthropic?

Tu dossier gratis identifica a las personas que te entrevistarían — con su background, qué valoran y cómo escribirles para destacar antes de aplicar.

About Anthropic 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. 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. Key responsibilities Data Infrastructure & Modeling • 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 Pipeline Development & Integration • 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 Analytics Engineering & Modeling • 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 Minimum qualifications • 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 Preferred qualifications • 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 n

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