Engineering Manager, Data Infrastructure

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.

Anthropic is looking for an Engineering Manager to lead and scale our Data Warehouse & Streaming Infra team. You'll own the data platform that Anthropic runs on: the foundation every team relies on to understand the business, make decisions, and keep our systems safe. Every org is your customer. As data volumes grow rapidly, and more of it arrives as real-time streams across multiple clouds, you'll help your team make that stack faster, more robust, and ready for what's next, and you'll shape the long-term vision for how data flows through one of the fastest-growing companies ever.

This is a high-visibility, high-impact role that calls for both deep technical judgment and strong people skills. You'll partner with leaders across finance, data science, product, engineering, and research to uncover and meet their needs, and you'll grow a small, strong core into a large team. We're looking for someone who is comfortable with ambiguity, energized by both business and technical impact, and cares deeply about people.

• Lead, grow, and mentor the Data Warehouse & Streaming Infra team, fostering a culture of ownership, collaboration, engineering excellence, and execution at speed

• Own Anthropic's data warehousing, streaming, 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, event streaming, change data capture, storage, orchestration, compute, query, and reporting systems at pace with business growth

• Collaborate to define and execute the roadmap for Anthropic's batch and streaming data infrastructure, balancing immediate business needs with durable, scalable design

• Lead key platform decisions for the streaming backbone, such as managed versus self-operated Kafka, grounded in clear models of throughput, cost, and operational burden

• 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, freshness and delivery SLAs, and operational processes that guide engineers and users through our high-change environment

• Ensure sound infrastructure investment decisions with clear awareness of cost, capacity, 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

• Have 3+ years of engineering management experience, with a track record of building and leading high-performing data infrastructure teams

• Are a people-first leader who gives direct feedback, grows engineers' careers, and builds trust with technical and non-technical partners alike, while staying steady and principled as priorities shift, knowing when to move fast and when to do it right

• Bring deep, hands-on expertise in both batch and streaming data infrastructure, from warehousing, pipelines, and orchestration to event streaming and change data capture, including the fundamentals of distributed log systems such as partitioning, delivery guarantees, and back pressure

• Have owned systems with significant business or financial impact, and shipped at speed while improving reliability, scalability, security, and cost

• Excel at hiring — you've built teams from small to large, and have sharp instincts for identifying exceptional talent

• Warehouse and batch technologies such as Big Query, Snowflake, Iceberg, Spark, dbt, or Airflow

• Streaming and change data capture technologies such as Kafka, Pub/Sub, Flink, or Debezium, ideally including running them at high scale

• Multi-cloud (GCP, AWS, Azure) or multi-region data platforms, including data-residency requirements

• 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…