Anthropic
Recruiting Analytics Data Engineer
$285K–$380K
San Francisco, CA +3 · Full-time · Posted Sep 14, 2026
- Location
- San Francisco, CA
New York City, NY
Seattle, WA
San Francisco, California, United States - Experience
- Minimum years correlate with internal job level requirements; preferred qualification: 5+ years in data engineering.
- Education
- Bachelor’s degree or an equivalent combination of education, training, and/or experience; field relevant to the role as demonstrated through coursework, training, or professional experience.
- Visa sponsorship
- Visa sponsorship is available, but not guaranteed for every role or candidate; the company will make reasonable efforts to obtain a visa if an offer is made.
About the job
Build and maintain the data infrastructure, pipelines, and models that support recruiting analytics and evidence-based workforce decisions.
What you'll do
- Design and optimize data architectures, dimensional models, governance, and security for recruiting and HR data.
- Build and monitor ETL/ELT pipelines integrating Workday, Greenhouse, and internal tools.
- Develop semantic layers, documentation, and standardized recruiting metrics for self-serve analytics.
What they ask for
- Expertise in BigQuery, including optimization and partitioning.
- Experience building dimensional models and understanding slowly changing dimensions.
- Proficiency in SQL, Python, dbt, and Fivetran.
- Experience implementing data security and privacy controls in cloud warehouses.
Nice to have
- Familiarity with ATS platforms such as Greenhouse or Lever.
- Experience with semantic layers for data agents, survey/text analytics pipelines, graph databases, privacy-enhancing technologies, or workforce planning.
Pay
$285,000 — $380,000 USD (base salary)
Tools
BigQuery, SQL, Python, dbt, Fivetran, Workday, Greenhouse, Lever
AI focus
Build data infrastructure supporting AI-driven data insights and collaborate with a team experimenting with AI to improve workforce understanding and support; semantic layers for data agents are preferred experience.
Model-written summary · the original posting is authoritative