Data Engineering Manager, Product at Anthropic

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

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Data Engineering Manager focused on Product at Anthropic, responsible for leading the analytics engineering team to build scalable data foundations, oversee data pipelines and models, partner with product stakeholders, and drive technical excellence.

Salary

USD 405,000 - 485,000

Requirements

Skills

  • 8+ years of experience managing analytics engineering or data engineering teams
  • 10+ years of total experience in analytics engineering, data engineering, or similar data-focused roles
  • Deep expertise in data modeling, ETL pipelines, and data warehouse architecture
  • Strong technical foundation with expertise in SQL, Python, dbt, and modern data stack tools
  • Proven track record of building and leading high-performing teams
  • Experience partnering with Data Science, Product, and Engineering leaders to deliver key product metrics and user behavior insights
  • Demonstrated ability to balance strategic thinking with hands‑on technical leadership
  • Strong communication skills with the ability to translate complex technical concepts for diverse audiences
  • Experience scaling analytics functions from early stage to maturity in rapidly changing environments
  • Track record of establishing data governance, quality standards, and best practices
  • Bias for action and urgency
  • Full‑stack mindset
  • Passion for Anthropic’s mission

Responsibilities

  • Build and scale the Product Analytics Engineering team, including hiring and mentoring a team of high‑performing analytics engineers embedded with Product pillars
  • Define and execute the strategic roadmap for product data foundations and analytics capabilities
  • Oversee the design and implementation of scalable data pipelines, data models, and analytics solutions that transform raw product event logs into canonical datasets and insightful data marts
  • Partner with Data Science, Product, and Engineering leadership to understand data needs and translate them into technical requirements
  • Establish and maintain high data integrity standards, SLAs, alerting, and best practices for the team
  • Drive the development of foundational data products, dashboards, and tools to enable self‑serve analytics; partner with the Data Science team to build innovative data tools using Claude to scale data‑driven decisions across Product teams
  • Foster a culture of technical excellence, continuous learning, and data‑driven decision making
  • Serve as a technical thought leader for data modeling, ETL processes, and product analytics infrastructure

Technologies

SQLPythondbtmodern data stack toolsClaude

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