Data Engineer, Safeguards

The shape of it

In the posting’s own words

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.

What it asks for · 5

• 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

Also a plus

• 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

What the job covers

• 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

Tools and skills named

Words the posting leans on

• data25×
• experience8×
• model7×
• safety7×
• build4×
• safeguards4×
• systems4×
• develop3×
• enforcement3×
• engineers3×
• ensure3×
• experience building3×
• frameworks3×
• monitoring3×
• pipelines3×
• similar3×

Counted from the posting after the mission statement and the legal notices are set aside. The ones near the top are the ones a screener is looking for.

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