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San Francisco, California, United States
Hybrid
Available
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 backpressure
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 BigQuery, 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 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
The annual compensation range for this role is listed below.
For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.
Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience
Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience
Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position
Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.
We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.
Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.
We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.
The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.
Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.
