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Salary: USD 405,000 - 485,000 / annual
Anthropic is seeking an Engineering Manager to lead and scale the Data Warehouse & Streaming Infrastructure team. This is a high-visibility, high-impact role responsible for owning the data platform that powers the entire organization—the foundation every team relies on to understand the business, make decisions, and keep systems safe.
You will lead, grow, and mentor a small, strong core team into a large organization, fostering a culture of ownership, collaboration, engineering excellence, and execution at speed. Your responsibilities include:
• Own Anthropic's data warehousing, streaming, and processing capabilities end-to-end: user experience, operations, reliability, security, governance, cost, and long-term strategic vision
• Support the team to scale and evolve 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 batch and streaming data infrastructure, balancing immediate business needs with durable, scalable design
• Lead key platform decisions (e.g., managed vs. 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 a high-change environment
• Ensure sound infrastructure investment decisions with clear awareness of cost, capacity, reliability, and long-term maintainability tradeoffs
• Align the broader Infrastructure organization on a common direction for shared platforms, tooling, and best practices across Anthropic's data stack
As data volumes grow rapidly and more data 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. You'll shape the long-term vision for how data flows through one of the fastest-growing companies ever.
This role calls for both deep technical judgment and strong people skills. You'll be comfortable with ambiguity, energized by both business and technical impact, and care deeply about people.
REQUIREMENTS:
• 3+ years of engineering management experience with a track record of building and leading high-performing data infrastructure teams
• People-first leadership: give direct feedback, grow engineers' careers, build trust with technical and non-technical partners, stay steady and principled as priorities shift, knowing when to move fast and when to do it right
• Deep, hands-on expertise in both batch and streaming data infrastructure: warehousing, pipelines, orchestration, event streaming, change data capture, and fundamentals of distributed log systems (partitioning, delivery guarantees, backpressure)
• Owned systems with significant business or financial impact; shipped at speed while improving reliability, scalability, security, and cost
• Excellence at hiring: built teams from small to large with sharp instincts for identifying exceptional talent
• Bachelor's degree or equivalent combination of education, training, and/or experience in a field relevant to the role
STRONG CANDIDATES MAY ALSO HAVE:
• Experience with warehouse and batch technologies: BigQuery, Snowflake, Iceberg, Spark, dbt, Airflow
• Streaming and change data capture technologies: Kafka, Pub/Sub, Flink, Debezium, ideally at high scale
• Multi-cloud (GCP, AWS, Azure) or multi-region data platforms, including data-residency requirements
• Data infrastructure at AI/ML-intensive companies: served customer teams building pipelines for financial/billing data, model training, evaluation, or safety workflows
• Building and operating observability or monitoring for data systems at scale
• Experience in high-growth environments where data infrastructure evolved rapidly to keep pace with business
