Staff Data Engineer, Trust and Safety Tech and Data Enablement
Washington D.C., DC, USA +2 · Staff · Full-time · Posted Oct 30, 2026
- Location
- Washington D.C., DC, USA
Atlanta, GA, USA
Austin, TX, USA - Experience
- 10 years in coding for datasets; 10 years with data processing software and algorithms; preferred: 10 years designing scalable pipelines and dimensional models.
- Education
- Bachelor's degree in Computer Science, Mathematics, a related field, or equivalent practical experience.
About the job
Leads the design and development of scalable, AI-ready data infrastructure and pipelines for Google’s Legal Content Policy and Standards team to support regulatory compliance and user safety.
What you'll do
- Define data sources and validation standards with partners, and collaborate with engineers and data scientists on ML training.
- Build secure, scalable infrastructure, data models, pipelines, and exchange formats to reduce fragmentation and improve discoverability.
- Deploy and train ML models, develop storage and serving systems, and analyze data to support requirements and decision-making.
What they ask for
- Bachelor's degree in Computer Science, Mathematics, a related field, or equivalent practical experience.
- 10 years of experience writing code for datasets using specialized languages and libraries such as Python, R, or SQL.
- 10 years of experience with data processing software such as Hadoop, Spark, Pig, or Hive, and algorithms such as MapReduce or Flume.
- Experience managing client-facing projects, troubleshooting technical issues, and working with Engineering and Sales Services teams.
Nice to have
- 10 years of experience designing robust, scalable, synchronized pipelines and dimensional modeling.
- Experience translating ambiguous business or regulatory requirements with non-technical stakeholders into technical designs.
Pay
USD 186000 - 269000 base salary
Tools
Python, R, SQL, Hadoop, Spark, Pig, Hive, MapReduce
AI focus
Supports ML training in collaboration with engineers and data scientists; deploys and continuously trains ML models. Develops AI-ready data infrastructure.
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