Data Engineer, Product

About the role
As a Data Engineer on the Data Science & Analytics team, you'll build the foundation that lets analytics scale across Anthropic. You'll partner with Engineering, Product and other teams to turn raw data into reliable metrics, reporting and insights, and you'll make sure teams have accurate metrics for our consumer products from idea to launch. You'll also lead your own projects that make self-serve insights possible, so teams can make data-driven decisions.

Responsibilities

• Understand, and where possible anticipate, the data needs of partner teams, and translate them into data models, reporting and technical requirements

• Define, build and manage key dbt pipelines that turn raw logs into canonical datasets

• Set data integrity standards and SLAs so data is delivered on time and accurately

• Build reliable dashboards that track core metrics and share insights across the company

• Build foundational data products, dashboards and tools that let self-serve analytics scale

• Partner with stakeholders to define and materialize metrics and analysis for new and evolving consumer products

• Shape Product teams' roadmaps from a data systems perspective

• Become an expert in our data models and data architecture

You may be a good fit if you have

• Significant experience as a Data Engineer or in a similar Data Science & Analytics role, ideally partnering with Product leads to build and report on company-wide metrics

• A passion for Anthropic's mission of building helpful, honest and harmless AI

• Expertise building multi-step ETL jobs with tools like dbt, plus experience with workflow tools like Airflow and version control through GitHub

• Expertise in SQL and Python for turning data into accurate, clean data models

• Experience building reporting and dashboards in tools like Hex that serve multiple cross-functional teams

• A bias for action, and a sense of when "good enough" beats perfect

• An end-to-end mindset: you take ownership of solving a problem fully, even when that means picking up work beyond your usual scope

• Comfort with ambiguity, and a habit of creating clarity and forward progress

• Experience using AI to scale your own productivity and your team's without lowering the quality of the work

Strong candidates may also have

• Experience building a data engineering (or similar) function from the ground up in an early-stage or fast-growing environment