Data Engineer, Anthropic

• Proficiency in SQL and Python, with hands-on experience building and maintaining ETL/ELT pipelines
• Experience with cloud data platforms such as BigQuery, Redshift, Snowflake, or similar
• Experience with modern data stack tools such as dbt, Airflow, Spark, or similar orchestration and transformation frameworks
• Experience building dashboards and data visualizations using tools such as Looker, Tableau, or Metabase
• Ability to communicate clearly and translate complex data concepts for both technical and non-technical audiences
• We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work

Desirable

• As a Data Engineer on the Safeguards team, you will build the data foundations that keep our AI systems safe
• The Safeguards team works to monitor models, prevent misuse, and ensure user well-being — and doing that well requires robust, reliable data infrastructure
• In this role, you will design and build the pipelines, warehousing solutions, and analytical tooling that power our safety and trust efforts at scale
• You'll work closely with engineers, data scientists, and policy teams to ensure the Safeguards organization has the data it needs to detect abuse patterns, measure the effectiveness of safety interventions, and make informed decisions about model behavior and enforcement
• This is a high-impact role where your work directly supports Anthropic's mission to develop AI that is safe and beneficial
• Design, build, and maintain scalable data pipelines that support safety monitoring, abuse detection, and enforcement workflows
• Develop and optimize data models and warehousing solutions to enable efficient analysis of large-scale usage and safety data
• Build and maintain dashboards and reporting infrastructure that give Safeguards teams visibility into model behavior, misuse patterns, and enforcement outcomes
• Collaborate with engineers to integrate data from multiple sources — including model outputs, user reports, and automated classifiers — into a unified analytical layer
• Implement data quality frameworks, monitoring, and alerting to ensure the reliability of safety-critical data
• Partner with research teams to surface data insights that inform model improvements and safety interventions
• Develop self-service data tooling that enables stakeholders to explore safety data and generate reports independently
• Contribute to data governance practices, including access controls, retention policies, and privacy-compliant data handling