Anthropic
Data Engineer
About this role
Anthropic is seeking an Analytics Engineer to build foundational data infrastructure and enable data-driven decision-making across the organization. You'll design and manage data pipelines, create company-wide dashboards, and establish standards for reliable metrics while collaborating with Engineering, Product, and GTM teams.
What you'll do
- Translate stakeholder data needs into technical requirements for key data models and reporting
- Build and manage ETL pipelines in dbt to transform raw logs into canonical datasets
- Establish data integrity standards and SLAs for timely, accurate data delivery
- Develop dashboards tracking core company metrics and performance indicators
- Create self-serve analytics products and tools to scale insights across teams
- Advise Product and GTM roadmaps from a data systems perspective
What they're looking for
- ETL pipeline development and dbt
- SQL and Python for data transformation
- Workflow orchestration tools like Airflow
- Data visualization and dashboarding (e.g., Hex)
- Data modeling and architecture
- GitHub and version control
- Cross-functional stakeholder collaboration
- Full-stack problem-solving mindset
Benefits
- Annual salary $320,000 - $405,000 USD
- Visa sponsorship available
- Hybrid work with 25% minimum office presence
- Early-stage role building analytics function at growing AI company
- Opportunity to shape company-wide data strategy
- Work on mission-driven AI safety initiatives
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Anthropic
Anthropic builds Claude, an AI assistant, and is hiring for engineering roles across infrastructure, data systems, and security that support both AI research operations and the company's internal technology needs. The company seeks infrastructure engineers, systems integrators, data scientists, and security specialists to build production-scale systems for training data pipelines, financial operations, developer productivity measurement, research infrastructure, and server firmware security.
- Website
- anthropic.com
Likely interview questions
- Walk us through your experience building and managing ETL pipelines with dbt. How do you ensure data quality and reliability at scale?
- Describe a time you partnered with Product or GTM stakeholders to understand their data needs. How did you translate those requirements into technical solutions?