Used Tools & Technologies
Not specified
Required Skills & Competences
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Python @ 6
SQL @ 6
dbt @ 6
ETL @ 3
Data Science @ 3
Hiring @ 3
Leadership @ 3
People Management @ 3
Communication @ 6
Data Engineering @ 3
Mentoring @ 3
Technical Leadership @ 3
Claude Code @ 3
AI @ 3
Data Modeling @ 3
Data Pipelines @ 3
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. The team includes researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.
Role summary
As an Analytics 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 development of scalable data solutions for Product pillars (Consumer, Claude Code, Enterprise & Verticals, Growth, Platform Product), manage a team of analytics engineers, and work closely with stakeholders across Data Science, Product, and Engineering to ensure reliable, accurate metrics that scale with company growth. The role balances hands-on technical leadership with people management and strategic vision for product data foundations.
Responsibilities
- Build and scale the Product Analytics Engineering team, including hiring and mentoring analytics engineers embedded with Product pillars
- Define and execute strategic roadmap for product data foundations and analytics capabilities
- Oversee design and implementation of scalable data pipelines, data models, and analytics solutions that transform raw product event logs into canonical datasets and data marts
- Partner with Data Science, Product, and Engineering leadership to translate data needs into technical requirements
- Establish and maintain data integrity standards, SLAs, alerting, and best practices
- Drive development of foundational data products, dashboards, and self-serve analytics tools; partner with Data Science to build data tools using Claude to scale product decisions
- 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
Requirements
- 5+ years managing analytics engineering or data engineering teams (preferably in a scaling startup)
- 8+ years 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
- Experience partnering with Data Science, Product, and Engineering to deliver product metrics and user behavior insights
- Proven track record of building and leading high-performing teams; experience establishing data governance, quality standards, SLAs, and best practices
- Ability to balance strategic thinking with hands-on technical leadership and a full-stack mindset
- Strong communication skills and a bias for action
Compensation
- Annual Salary: $370,000 - $450,000 USD
Logistics
- Locations listed: San Francisco, CA; New York City, NY; Seattle, WA (United States)
- Location-based hybrid policy: staff are expected to be in one of Anthropic's offices at least 25% of the time
- Education: Minimum of a Bachelor's degree in a related field or equivalent experience
- Visa sponsorship: Anthropic states they do sponsor visas and retain an immigration lawyer to assist, though not every role/candidate can be successfully sponsored
How we're different
Anthropic emphasizes large-scale, collaborative AI research focused on steerable, trustworthy AI and values communication and cross-functional collaboration. The team works on a few large-scale research efforts and hosts frequent research discussions.