Analytics Data Engineering Manager, Product
crypto:analyticsengineeringM2Data Science & Analytics
Compensation
Not disclosed
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 an Analytics 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 that transform raw product event logs into canonical datasets and insightful data marts
Partner with Data Science, Product, and Engineering leadership to unde