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.
As a Data Engineering Manager focused on Product, you will build and lead the analytics engineering team responsible for creating the data foundations that enable data-driven decision making across Anthropic’s Product organization. You will oversee the development of scalable data solutions for Product pillars – including Consumer, Claude Code, Enterprise & Verticals, Growth, Platform Product – managing a team of analytics engineers and working closely with stakeholders across Data Science, Product, and Engineering to ensure teams have access to reliable, accurate metrics that can scale with our company’s growth.
In this role, you will balance hands-on technical leadership with people management, setting the strategic vision for product data foundations while developing and mentoring team members. You will partner closely with Product Data Scientists, Product Managers, and Product Engineers to understand how users interact with Claude, how to measure product quality and growth, and how to transform raw event logs into insightful data marts that power product decisions.
Build and scale the Product Analytics Engineering team, including hiring and mentoring a team of high-performing analytics engineers embedded with Product pillars
Define and execute the strategic roadmap for product data foundations and analytics capabilities
Oversee the design and implementation of scalable data pipelines, data models, and analytics solutions that transform raw product event logs into canonical datasets and insightful data marts
Partner with Data Science, Product, and Engineering leadership to understand data needs and translate them into technical requirements
Establish and maintain high data integrity standards, SLAs, alerting, and best practices for the team
Drive the development of foundational data products, dashboards, and tools to enable self-serve analytics; partner with the Data Science team to build innovative data tools using Claude to scale data-driven decisions across Product teams
Foster a culture of technical excellence, continuous learning, and data-driven decision making
Serve as a technical thought leader for data modeling, ETL processes, and product analytics infrastructure
8+ years of experience managing analytics engineering or data engineering teams, preferably in a scaling startup environment
10+ years of total experience in analytics engineering, data engineering, or similar data-focused roles
Deep expertise in data modeling, ETL pipelines, and data warehouse architecture
Strong technical foundation with expertise in SQL, Python, dbt, and modern data stack tools
Proven track record of building and leading high-performing teams
Experience partnering with Data Science, Product, and Engineering leaders to deliver key product metrics and user behavior insights
Demonstrated ability to balance strategic thinking with hands-on technical leadership
Strong communication skills with the ability to translate complex technical concepts for diverse audiences
Experience scaling analytics functions from early stage to maturity in rapidly changing environments
Track record of establishing data governance, quality standards, and best practices
A bias for action and urgency, not letting perfect be the enemy of the effective
A “full-stack mindset”, not hesitating to do what it takes to solve a problem end-to-end
A passion for Anthropic’s mission of building helpful, honest, and harmless AI
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.