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Analytics Engineer

Anthropic LogoAnthropic
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Salary

Annual Salary: $200,000 - $240,000 USD

As an Analytics Engineer, you will be an early member of the Data Science & Analytics team building the foundation to scale analytics across our GTM and Product teams. You will collaborate with key stakeholders in GTM and Product to build scalable solutions to transform data into key metrics reporting and insights. You will be responsible for ensuring teams have access to reliable, accurate metrics that can scale with our company’s growth. You will also lead your own projects to enable self-serve insights to help teams make data-driven decisions.

Responsibilities:

  • Understand the data needs of GTM and Product teams in terms of key data models and reporting, and translate that into technical requirements
  • Own our dbt instance, defining and maintaining key data pipelines and marts that power core company-wide metrics
  • Establish high data integrity standards and SLAs to ensure timely, accurate delivery of data
  • Build insightful and reliable dashboards to track performance of core metrics that will deliver insights to the whole company
  • Build foundational data products and tools to enable self-serve analytics to scale across the company
  • Influence the future roadmap of Product and GTM teams from a data systems perspective
  • Become an expert in our GTM and Product data models and the company's data architecture

You may be a good fit if you have:

  • 5+ years of experience as an Analytics Engineer or similar Data Science & Analytics roles, preferably partnering with GTM and Product leads to build and report on key company-wide metrics.
  • A passion for the company's mission of building helpful, honest, and harmless AI
  • Expertise in building multi-step ETL jobs through tooling like dbt and orchestrated through workflow management platforms like Airflow
  • Expertise in SQL and Python to transform data into accurate, clean data models
  • Experience building data reporting and dashboarding in visualization tools to serve multiple cross-functional teams
  • 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, even if it requires going outside the original job description
  • Experience building Analytics Engineering (or similar) function at start-ups
  • A strong disposition to thrive in ambiguity, taking initiative to create clarity and forward progress

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