Data Engineer

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. About the Role As a Data Engineer, you will be an early member of the Data Science & Analytics team building the foundation to scale analytics across our organization. You will collaborate with key stakeholders in Engineering, Product, GTM and other areas 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 stakeholder teams in terms of key data models and reporting, and translate that into technical requirements · Define, build and manage key data pipelines in dbt that transform raw logs into canonical datasets · Establish high data integrity standards and SLAs to ensure timely, accurate delivery of data · Develop insightful and reliable dashboards to track performance of core metrics that will deliver insights to the whole company · Build foundational data products, dashboards 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 organization’s data models and the company's data architecture You might be a good fit if you have: · 5+ years of experience as a Data 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, building robust data models through tooling like dbt; proficiency with workflow management platforms like Airflow and version control management tools through GitHub. · Expertise in SQL and Python to transform data into accurate, clean data models. · Experience building data reporting and dashboarding in visualization tools like Hex 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 an Analytics Data Engineering (or similar) function at start-ups. · A strong disposition to thrive in ambiguity, taking initiative to create clarity and forward progress. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $320,000—$405,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.