Data Engineer, Economic Research

Anthropic•San Francisco, CA

2h•$230,000 - $275,000•Hybrid

About The Position

As a Data Engineer on the Economic Research team, you will build, maintain and lead the critical data engineering that powers Anthropic's research on AI's economic impact. You will work with data systems from across Anthropic, including our research tools for privacy-preserving analysis. You will work at the intersection of data engineering and software engineering. In this role, you will work closely with teams across Anthropic—including Data Science and Analytics, Data Infrastructure, Clio Engineering, Societal Impacts, and Public Policy—to build scalable and robust data systems that support high-leverage, high-impact research. Your work will enable economists and researchers—both internal and external—to understand how AI is transforming economic activity. You will have an opportunity to shape the direction of data foundations for economic research at Anthropic during a period of fast growth.

Requirements

5+ years of experience as a Data Engineer, Analytics Engineer, or Software Engineer working on data engineering or data foundations, with a track record of building and maintaining production systems
Have experience with data infrastructure and large datasets in production environments.
Have experience with cloud infrastructure platforms such as AWS or GCP.
Design, implement and test data models for economic research.
A passion for Anthropic's mission of building helpful, honest, and harmless AI and understanding its economic implications.
Strong programming skills in Python and SQL, with experience building production-quality data pipelines and analytical tools.
Demonstrated ability to translate ambiguous questions into concrete technical requirements and scalable solutions.
A bias for action and urgency, not letting perfect be the enemy of the effective.
A “full-stack mindset”, not hesitating to do what it takes to solve a problem end-to-end, even if it requires going outside the original job description.
Strong communication skills to collaborate effectively with economists, researchers, and cross-functional partners who may have varying levels of technical expertise.

Nice To Haves

Background in econometrics, statistics, or quantitative social science research
Experience building data infrastructure and data foundations for research
Familiarity with large language models, AI systems, or ML research workflows
Prior work on projects related to labor economics, technology adoption, or economic measurement

Responsibilities

Build and maintain data pipelines that process large scale Claude usage logs into canonical, reusable datasets while maintaining user privacy.
Expand privacy-preserving tools to enable new analytic functionality to support research needs.
Design and build upon novel data systems leveraging language models (e.g., CLIO) where traditional data engineering patterns don't yet exist.
Develop and maintain data pipelines that are interoperable across data sources (including ingesting external data) and are designed to support economic analysis.
Lead the strategic development of the economic research data foundations roadmap
Ensure data reliability, integrity, and privacy compliance across all economic research pipelines and retention runs.
Create documentation and best practices that enable self-serve data access for researchers while maintaining security and governance standards.
Collaborate with the broader Data Science and Analytics and Data Infrastructure teams to align on tooling, infrastructure patterns, and shared data resources.