People Analytics Data Engineer at ANTHROPIC PBC DBA ANTHROPIC INC

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Full Time

Start Date

Immediate

Expiry Date

16 Apr, 26

Salary

245000.0

Posted On

16 Jan, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Yes

Skills

BigQuery, SQL, Python, Data Engineering, Data Governance, ETL, ELT, Data Modeling, Data Security, HRIS, Analytics Engineering, Organizational Network Analysis, Data Quality, Documentation, Data Architecture, AI

Industry

Research Services

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. 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. Responsibilities: Data Infrastructure & Modeling 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 Pipeline Development & Integration 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 Analytics Engineering & Modeling 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 You may be a good fit if you: 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 Strong candidates may also have: 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 Application Deadline: 6pm PT on October 26, 2025. We encourage all interested and qualified applicants to submit their materials before this date to ensure full consideration. The annual compensation range for this role is 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. Our total compensation package for full-time employees includes equity and benefits. Annual Salary: $175,000—$245,000 USD Logistics Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience. 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. 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

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Responsibilities

The People Analytics Data Engineer will build and maintain the data infrastructure for people analytics, focusing on scalable data architectures and robust data models. This role involves optimizing existing data tables and ensuring data governance and access controls.