Analytics Data Engineering Manager, Product

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.

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.

• 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

• 5+ years of experience managing analytics engineering or data engineering teams, preferably in a scaling startup environment
• 8+ 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
• 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
• A passion for Anthropic’s mission of building helpful, honest, and harmless AI

$370,000—$450,000 USD

Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience.

Location‑based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

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