[P] Data Engineer, Safeguards

Key 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

Minimum Qualifications

• 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

Preferred Qualifications

• 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

Compensation

Annual Salary: $320,000—$405,000 USD

Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience