Data Engineer, Economic Research

Anthropic Anthropic · AI Frontier · AI Policy & Societal Impacts

Data Engineer on the Economic Research team responsible for building and maintaining data pipelines for AI's economic impact research. This involves processing large-scale usage logs, expanding privacy-preserving tools, designing novel data systems leveraging language models, and ensuring data reliability and privacy compliance. The role collaborates with various teams to support economic analysis and shape the data foundations roadmap.

What you'd actually do

  1. Build and maintain data pipelines that process large scale Claude usage logs into canonical, reusable datasets while maintaining user privacy.
  2. Expand privacy-preserving tools to enable new analytic functionality to support research needs.
  3. Design and build upon novel data systems leveraging language models (e.g., CLIO) where traditional data engineering patterns don't yet exist.
  4. Develop and maintain data pipelines that are interoperable across data sources (including ingesting external data) and are designed to support economic analysis.
  5. Lead the strategic development of the economic research data foundations roadmap

Skills

Required

  • Python
  • SQL
  • Data Engineering
  • Data Modeling
  • Cloud Infrastructure (AWS/GCP)
  • Production Systems
  • Large Datasets
  • Data Pipelines
  • Privacy-Preserving Tools
  • Language Models

Nice to have

  • Analytics Engineering
  • Software Engineering
  • External Data Ingestion
  • Economic Analysis Support
  • Self-Serve Data Access
  • Security and Governance Standards

What the JD emphasized

  • critical data engineering
  • scalable and robust data systems
  • high-leverage, high-impact research
  • novel data systems leveraging language models
  • economic research data foundations roadmap
  • data reliability, integrity, and privacy compliance
  • production systems
  • large datasets in production environments
  • production-quality data pipelines

Other signals

  • build scalable and robust data systems
  • shape the direction of data foundations
  • process large scale Claude usage logs
  • leverage language models (e.g., CLIO)
  • interoperable across data sources
  • economic research data foundations roadmap
  • data reliability, integrity, and privacy compliance
  • enable self-serve data access for researchers