[P] Data Engineer, Safeguards at Anthropic

Hybrid - San Francisco, CA, USA

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The Data Engineer on the Safeguards team will design and build the data foundations that keep Anthropic’s AI systems safe. Responsibilities include creating scalable pipelines, optimizing data models, building dashboards, integrating diverse data sources, implementing quality frameworks, partnering with research teams, developing self‑service tooling, and contributing to data governance. The role supports safety monitoring, abuse detection, and enforcement workflows, directly impacting Anthropic’s mission to create reliable and beneficial AI.

Salary

USD 320,000 - 405,000

Requirements

Skills

  • 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
  • 8+ years of experience in data engineering, analytics engineering, or a related role
  • Comfort contributing across the stack and picking up work outside your immediate scope when the situation calls for it
  • Background in trust and safety, integrity, fraud, or abuse detection data systems
  • Experience with large‑scale event streaming systems such as Kafka, Pub/Sub, or Kinesis
  • Experience building data infrastructure that supports ML model monitoring or evaluation
  • Familiarity with data privacy and compliance frameworks such as GDPR, CCPA, or similar
  • Background in statistical analysis or experience working closely with data scientists
  • A genuine interest in the societal implications of AI and in making AI systems safer

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

Technologies

SQLPythonBigQueryRedshiftSnowflakedbtAirflowSparkLookerTableauMetabaseKafkaPub/SubKinesisGDPRCCPA

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