Job description

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 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 The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $285,000 — $380,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Requirements

Must-haves

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

Nice-to-haves

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
The annual compensation range for this role is listed below.
Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
How we're different
Come work with us!

Application prep

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