Data Engineer, Safeguards

About Anthropic 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 Anthropic is looking for a Data Engineer to join the Safeguards team and 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'll design and build the data 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 will directly support Anthropic's mission to develop AI that is safe and beneficial. Responsibilities: - 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 You may be a good fit if you: - Have 3+ years of experience in data engineering, analytics engineering, or a related role - Are proficient in SQL and Python, with experience building and maintaining ETL/ELT pipelines - Have hands-on experience with modern data stack tools such as dbt, Airflow, Spark, or similar orchestration and transformation frameworks - Have worked with cloud data platforms (BigQuery, Redshift, Snowflake, or similar) - Are comfortable building dashboards and data visualizations using tools like Looker, Tableau, or Metabase - Communicate clearly and can translate complex data concepts for both technical and non-technical audiences - Are results-oriented, flexible, and willing to pick up slack even when it falls outside your job description - Care about the societal impacts of AI and are motivated by safety work Strong candidates may have: - Experience with trust safety, integrity, fraud, or abuse detection data systems - Experience with large-scale event streaming systems (Kafka, Pub/Sub, Kinesis) - Built data infrastructure that supports ML model monitoring or evaluation - Familiarity with data privacy and compliance frameworks (GDPR, CCPA, or similar) - A background in statistical analysis, or experience collaborating closely with data scientists - Developed internal tooling or self-service analytics platforms Strong candidates need not have: - A formal degree in Computer Science or a related field — we value practical experience and demonstrated ability over credentials - Prior experience in AI or machine learning — you'll learn the domain-specific context on the job - Previous experience at an AI safety or research organization - Deep expertise across every tool l ... (truncated, view full listing at source)