Engineering Manager, Data Architecture

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 the Engineering Manager for Data Architecture, you will lead the team responsible for building our north star implementation of data infrastructure. This team owns the full lifecycle of Anthropic’s most critical data, ranging from customer-facing data like prompts and exchanges (highly PII) to core business data such as logging and financial records. Your mission is to grow this infrastructure into a world-class platform that powers our rapidly expanding business while maintaining the highest standards for AI safety and regulatory compliance. This is a high-leverage leadership role where you will architect systems that support model training with customer consent, legal reviews, and AI safety evaluations (Safe Guard). You will ensure our data platform is inherently secure, massively scalable, and flexible enough to support diverse product surfaces across multiple cloud environments. If you are passionate about building the foundational systems that enable a frontier AI lab to scale safely and efficiently, this role is for you. Key Responsibilities Build and lead the team: Recruit and mentor a team of world-class data and infrastructure engineers; establish the team’s technical vision, operational standards, and strategic roadmap. Drive technical strategy: Define the long-term architecture for Anthropic’s data stack, ensuring it supports high-velocity model training and complex inference workloads across all cloud regions. Architect scalable pipelines: Lead the design and implementation of robust, automated data pipelines that handle petabyte-scale datasets with high reliability and performance. Implement robust governance: Build the systems and processes for automated data discovery, lineage tracking, and lifecycle management to ensure high data quality and integrity. Security and compliance-by-design: Ensure dat a architecture inherently supports global privacy regulations and security requirements through automated controls and privacy-preserving architectures. Cross-functional enablement: Partner with ML, Product, and Legal teams to unlock the power of data, providing the tools and platforms needed to derive insights without compromising safety. Standardize data quality: Define and enforce SLAs for data availability and accuracy, building internal tools to monitor and maintain the health of the entire data ecosystem.