Anthropic’s Engineering Manager for Data Architecture will lead a high‑impact team responsible for building the company’s core data infrastructure. The role focuses on designing scalable, secure, and compliant data pipelines and platforms that support model training and inference across multiple cloud environments. The manager will define long‑term technical strategy, recruit and mentor engineers, and collaborate closely with product, ML, and legal teams to ensure the data architecture aligns with AI safety and regulatory compliance goals.
Engineering Manager, Data Architecture at Anthropic
Hybrid - San Francisco, CA; Seattle, WA
More jobs at AnthropicSalary
USD 405,000 - 485,000
Requirements
Skills
- Extensive experience managing and scaling engineering teams in high-growth environments, focused 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 support 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 engineering approach, balancing future‑proofing with immediate value through iterative improvements
- Excellent communication skills, able to explain complex architectural trade‑offs to technical and non‑technical stakeholders
- Comfort with end‑to‑end ownership and a desire to build a full‑stack data foundation serving the company as the single source of truth
- 8+ years of experience managing technical teams
- Experience growing an engineering team and charter through 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 hypergrowth, scaling privacy alongside business
- Exposure to AI/ML infrastructure and privacy demands of large‑scale training and inference
Responsibilities
- Build and lead the team: recruit and mentor a world‑class data and infrastructure engineering team; establish technical vision, operational standards, and strategic roadmap
- Drive technical strategy: define long‑term architecture for Anthropic’s data stack to support high‑velocity model training and complex inference workloads across all cloud regions
- Architect scalable pipelines: lead design and implementation of robust, automated data pipelines handling petabyte‑scale datasets with high reliability and performance
- Implement robust governance: build systems and processes for automated data discovery, lineage tracking, and lifecycle management to ensure high data quality and integrity
- Ensure security and compliance‑by‑design: embed global privacy regulations and security requirements through automated controls and privacy‑preserving architectures
- Cross‑functional enablement: partner with ML, Product, and Legal teams to provide tools and platforms for deriving insights without compromising safety
- Standardize data quality: define and enforce SLAs for data availability and accuracy; build internal tools to monitor and maintain the health of the entire data ecosystem
- Advocate for modern data architecture: communicate progress and risks to leadership and cross‑functional stakeholders, reinforcing its importance to AI safety
Technologies
Cloud‑native data platformsMulti‑cloud deploymentsData pipelinesData modelingDatabase internalsData warehouse / lakehouse architecturesData governance (metadata management, lineage, quality enforcement)Privacy and security controls
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