Engineering Manager, Data Architecture

Anthropic•Seattle, WA

15h•$405,000 - $485,000•Hybrid

About The Position

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.

Requirements

Extensive experience managing and scaling engineering teams in high-growth environments, with a focus on data infrastructure or distributed systems.
Deep technical expertise in data modeling, database internals, and large-scale data warehouse/lakehouse architectures.
Proven track record of architecting cloud-native, scalable data platforms that handle multi-cloud deployments and high-throughput data streams.
Strong foundation in data governance principles, including metadata management, data lineage, and automated quality enforcement at scale.
Ability to thrive in high-ambiguity environments, translating broad business goals into specific technical roadmaps and actionable engineering tasks.
Pragmatic approach to engineering: you know when to build for the future and when to deliver immediate value through iterative improvements.
Excellent communication skills, with the ability to explain complex architectural trade-offs to both technical and non-technical partners.
Comfort with end-to-end ownership and a desire to build a "full-stack" data foundation that serves as the company's single source of truth.

Nice To Haves

8+ years of experience managing technical teams
Experience growing an engineering team and charter through a period of rapid company scaling.
Experience conducting privacy reviews, threat modeling, and risk assessments for production systems
Proven track record of designing and implementing privacy infrastructure serving millions of users
Experience at companies during periods of hypergrowth where you've scaled privacy alongside the business
Exposure to AI/ML infrastructure and the unique privacy demands of large-scale training and inference

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 data 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.
Evangelize the data mission: Advocate for the importance of modern data architecture as a core component of AI safety, communicating progress and risks to leadership and cross-functional stakeholders.