About the role

Anthropic is looking for an Engineering Manager to lead and scale our Data Warehouse Infra team. You'll own the stack that powers Anthropic's business understanding: Every org is your customer, and your team enables coworkers to view and improve current state by applying complex business logic to massive datasets at speed. You'll take that stack to the next level, supporting engineers to add new capabilities and make everything even more robust and bulletproof, while handling rapidly increasing load. You'll work across the company to define and drive the long-term vision for how data flows through one of the fastest-growing companies ever.

This is a high-visibility, high-impact role. Your work will require top-notch people skills and cross-functional collaboration, as well as deep technical experience and judgment across both infrastructure and data science/analytics. You won't just execute: You and the engineers you support will partner closely with users, PMs, TPMs, and leadership across finance, data science, product, engineering, and research, to synthesize and consolidate needs, and identify and satisfy unmet needs. We're looking for someone who moves comfortably through ambiguity, is equally energized by business and technical impact, and cares deeply about people. You'll grow a small, strong core to a large team, advancing initiatives that are critical to the future of responsible, useful, reliable AI.

Responsibilities

• Lead, grow, and mentor a Data Warehouse Infra team, fostering a culture of ownership, collaboration, engineering excellence, and execution at speed
• Own Anthropic's data warehousing and processing capabilities end-to-end: "wow" user experience, ops, reliability/security/governance/cost, long-term strategic vision
• Support the team to scale and evolve our data ingestion, storage, orchestration, compute, query, and reporting systems at pace with business growth
• Collaborate to define and execute the roadmap for Anthropic's offline data infrastructure, balancing immediate business needs with durable, scalable design
• Partner closely across Finance, Product, Research, and Engineering to ensure data systems directly support business-critical decisions and company growth
• Drive hiring for the team — sourcing, evaluating, and closing senior data infrastructure engineers who thrive in high-growth, high-trust environments
• Establish data quality standards, SLAs, and operational processes that guide engineers and users through our high-change environment
• Ensure sound infrastructure investment decisions with clear awareness of cost, reliability, and long-term maintainability tradeoffs
• Align the broader Infrastructure org on a common direction for shared platforms, tooling, and best practices across Anthropic's data stack

You may be a good fit if you

• Have 3+ years of engineering management experience, with a track record of building and leading high-performing data infrastructure teams
• Bring deep domain expertise in offline data infrastructure: data warehousing, ETL/ELT pipelines, workload orchestration, batch compute, and reporting tooling
• Have owned systems or programs with significant financial impact, and you approach infrastructure decisions with a clear understanding of business outcomes
• Have shipped at speed while driving improvements in reliability, accuracy, scalability, security, toil reduction, and/or cost reduction
• Excel at hiring — you've built teams from small to large, and have sharp instincts for identifying exceptional talent
• Are a people-first leader who creates psychological safety, gives direct and actionable feedback, and loves to foster engineer career growth
• Communicate clearly and build trust across technical and non-technical stakeholders
• Are comfortable operating in a fast-moving environment where priorities shift, ambiguity is the norm, and the ability to pivot quickly while protecting invariants is a must-have
• Take a thoughtful, principled, impact-driven approach to building infrastructure — you know when to run fast, and when to take the time to do it right

Strong candidates may also have experience with

• Technologies such as Spark, Airflow, Dagster, dbt, EMR/Hadoop/Kubernetes, Iceberg, Hudi, Snowflake, BigQuery, Redshift, or similar data warehousing technologies
• Data infrastructure at AI or ML-intensive companies: you've served customer teams that build pipelines for financial/billing data, model training, evaluation, or safety workflows
• Building and operating observability or monitoring for data systems at scale
• Working in high-growth environments where data infrastructure had to evolve rapidly to keep pace with the business