Analytics Data Engineering Manager, Product

Rozgoo; New York (NY); Sur place; USD 180 000 - 240 000; Plein temps

Résumé du poste

Anthropic is seeking an Analytics Data Engineering Manager focused on Product to build and lead a team delivering data foundations for decision making across Product pillars such as Consumer, Claude Code, Enterprise & Verticals, Growth, and Platform Product.

You will balance hands-on technical leadership with people management, shaping the roadmap and mentoring engineers to transform raw event logs into usable data marts powering product decisions.

Qualifications

• 5+ years in analytics engineering or data engineering.
• Experience leading teams delivering product analytics.
• Strong data modeling and scalable data solutions.

Responsabilités

• Build and scale the Product Analytics Engineering team, hiring and mentoring 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,

Connaissances

Leadership

Analytics Engineering

Data Pipelines

Product Metrics

Team Management

Stakeholder Collaboration

Outils

SQL

Python

Spark

dbt

Airflow

Description du poste

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 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,