Primary record

Data Engineering Manager, Product

Anthropic Indexed employerSan Francisco, CA | New York City, NY | Seattle, WA · New York, New York, United States · San Francisco, California, United States · Seattle, Washington, United States
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Workplace

hybrid

Employment

Internship

Published

Aug 11, 2026

Closes

No date supplied

The role

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.

About the role

As a Data Engineering Manager focused on Product, you will build and lead the analytics engineering team responsible for creating the data foundations that enable data-driven decision making across Anthropic’s Product organization. You will oversee the development of scalable data solutions for Product pillars – including Consumer, Claude Code, Enterprise & Verticals, Growth, Platform Product – managing a team of analytics engineers and working closely with stakeholders across Data Science, Product, and Engineering to ensure teams have access to reliable, accurate metrics that can scale with our company’s growth.

In this role, you will balance hands-on technical leadership with people management, setting the strategic vision for product data foundations while developing and mentoring team members. You will partner closely with Product Data Scientists, Product Managers, and Product Engineers to understand how users interact with Claude, how to measure product quality and growth, and how to transform raw event logs into insightful data marts that power product decisions.

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 t

Requirements

Department: Data Science & Analytics