Data Engineer at Anthropic

Hybrid - San Francisco, CA, United States; New York City, NY, United States; Seattle, WA, United States

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As a Data Engineer, you will be an early member of the Data Science & Analytics team building the foundation to scale analytics across our organization. You will collaborate with key stakeholders in Engineering, Product, GTM and other areas to build scalable solutions to transform data into key metrics reporting and insights. You will be responsible for ensuring teams have access to reliable, accurate metrics that can scale with our company’s growth and lead your own projects to enable self‑serve insights to help teams make data‑driven decisions.

Salary

USD 320,000 - 405,000

Requirements

Skills

  • 5+ years of experience as a Data Engineer or similar Data Science & Analytics roles, preferably partnering with GTM and Product leads to build and report on key company-wide metrics
  • A passion for the company's mission of building helpful, honest, and harmless AI
  • Expertise in building multi-step ETL jobs, building robust data models through tooling like dbt; proficiency with workflow management platforms like Airflow and version control management tools through GitHub
  • Expertise in SQL and Python to transform data into accurate, clean data models
  • Experience building data reporting and dashboarding in visualization tools like Hex to serve multiple cross‑functional teams
  • 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
  • Experience building an Analytics Data Engineering (or similar) function at start‑ups
  • A strong disposition to thrive in ambiguity, taking initiative to create clarity and forward progress

Responsibilities

  • Understand the data needs of stakeholder teams in terms of key data models and reporting, and translate that into technical requirements
  • Define, build and manage key data pipelines in dbt that transform raw logs into canonical datasets
  • Establish high data integrity standards and SLAs to ensure timely, accurate delivery of data
  • Develop insightful and reliable dashboards to track performance of core metrics that will deliver insights to the whole company
  • Build foundational data products, dashboards and tools to enable self‑serve analytics to scale across the company
  • Influence the future roadmap of Product and GTM teams from a data systems perspective
  • Become an expert in our organization’s data models and the company's data architecture

Technologies

dbtAirflowGitHubSQLPythonHex

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