Engineering Manager, Data Infrastructure

San Francisco, CA | New York City, NY · San Francisco · USD 405,000-485,000 / year · 3+ years experience · Hybrid · Posted Sep 22, 2026

At a glance

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 seeking an Engineering Manager to lead and scale the Data Warehouse & Streaming Infra team.
The role involves owning the data platform foundation, including data warehousing, streaming, and processing capabilities, to support business decisions and system safety.
The manager will partner with cross-functional leaders to define the long-term vision for data infrastructure in a high-growth environment.

Lead, grow, and mentor the Data Warehouse & Streaming Infra team.
Own Anthropic's data warehousing, streaming, and processing capabilities end-to-end.
Support the team to scale and evolve data ingestion, event streaming, change data capture, storage, orchestration, compute, query, and reporting systems.
Collaborate to define and execute the roadmap for batch and streaming data infrastructure.
Lead key platform decisions for the streaming backbone, such as managed versus self-operated Kafka.
Partner across Finance, Product, Research, and Engineering to ensure data systems support business-critical decisions.
Drive hiring for the team by sourcing, evaluating, and closing senior data infrastructure engineers.
Establish data quality standards, freshness and delivery SLAs, and operational processes.
Ensure sound infrastructure investment decisions regarding cost, capacity, reliability, and maintainability.
Align the broader Infrastructure org on a common direction for shared platforms, tooling, and best practices.

3+ years of engineering management experience with a track record of building and leading high-performing data infrastructure teams.
Deep, hands-on expertise in both batch and streaming data infrastructure, including warehousing, pipelines, orchestration, event streaming, and change data capture.
Understanding of distributed log systems fundamentals such as partitioning, delivery guarantees, and backpressure.
Experience owning systems with significant business or financial impact while improving reliability, scalability, security, and cost.
Proven ability to build teams from small to large and identify exceptional talent.
Bachelor’s degree or an equivalent combination of education, training, and/or experience in a relevant field.

Experience with warehouse and batch technologies such as BigQuery, Snowflake, Iceberg, Spark, dbt, or Airflow.
Experience with streaming and change data capture technologies such as Kafka, Pub/Sub, Flink, or Debezium.
Experience with multi-cloud (GCP, AWS, Azure) or multi-region data platforms.
Experience with data infrastructure at AI or ML-intensive companies.
Experience building and operating observability or monitoring for data systems at scale.
Experience working in high-growth environments where data infrastructure evolved rapidly.

Location-based hybrid policy requires staff to be in one of the offices at least 25% of the time.
Some roles may require more time in the office.

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