Data Engineer, Product Mid

D8 LooP; New York (NY); On-site; USD 120,000 - 180,000; Full time

Job summary

Anthropic is seeking a Data Engineer to advance our data infrastructure across multiple teams in San Francisco, New York City, and Seattle. You will own end-to-end ETL processes, model data, and build insights that inform product and policy decisions.

Collaborating with partners, you will set data quality standards, maintain SLAs, and implement reliable dashboards to track core metrics.

Qualifications

• Experience building multi-step ETL jobs with Airflow and dbt.
• Translate partner data needs into data models and reporting requirements.
• Proficient in Python and SQL for data modeling and validation.
• Define key dbt pipelines and ensure data delivery meets SLAs.

Responsibilities

• Build multi-step ETL jobs with Airflow and dbt pipelines.
• Turn raw logs into canonical datasets for analytics.
• Develop reliable dashboards to track core metrics and insights.
• Set data integrity standards and ensure timely data delivery.

Skills

Python

SQL

Dashboards

Data modeling

Data requirements understanding

Collaborating with partners

Tools

Airflow

dbt

GitHub

Job description

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.

San Francisco, CA | New York City, NY | Seattle, WA Mid Relocation / visa support in the posting Data Engineer

Skills, with evidence

• Airflow / orchestration Expertise building multi-step ETL jobs with tools like dbt, plus experience with workflow tools like Airflow and version control through GitHub must have
• Understand, and where possible anticipate, the data needs of partner teams, and translate them into data models, reporting and technical requirements must have
• Python Expertise in SQL and Python for turning data into accurate, clean data models must have
• SQL Expertise in SQL and Python for turning data into accurate, clean data models must have
• dbt Define, build and manage key dbt pipelines that turn raw logs into canonical datasets must have
• Build reliable dashboards that track core metrics and share insights across the company not practised here
• Set data integrity standards and SLAs so data is delivered on time and accurately