Waymo
Data Engineer
About this role
Waymo seeks a Data Engineer to design and build data warehouse and pipeline solutions that support the company's autonomous driving commercialization. You'll translate business requirements into scalable data models, ensure data quality and privacy compliance, and collaborate across teams to meet evolving data needs.
What you'll do
- Convert business requirements into conceptual, logical, and physical data models
- Design, build, and maintain data warehouse and pipeline solutions for raw data transformation
- Implement data quality frameworks and maintain comprehensive documentation
- Handle PII protection and access control compliance across data systems
- Partner with data producers and consumers to understand needs and provide technical consultation
- Optimize data solutions for reporting and commercialization insights
What they're looking for
- Data warehouse design (BigQuery, Snowflake)
- SQL and at least one of Python, C++, or Java
- Relational and NoSQL database knowledge
- Data modeling and governance
- Distributed processing frameworks (Spark, Hadoop, Kafka)
- Data privacy and security practices
- ETL pipeline development
- Cross-functional collaboration
Benefits
- Discretionary annual bonus program
- Equity incentive plan
- Generous company benefits package
- Work on cutting-edge autonomous driving technology
- Mountain View, California location
Opens the official application on the employer’s site. No login required.
Waymo
Waymo develops autonomous driving technology and vehicles, building the AI systems, simulation platforms, and infrastructure that power the Waymo Driver. The company is hiring for ML infrastructure engineers, platform engineers, labeling system developers, backend software engineers, and automotive systems engineers to scale its autonomous driving capabilities.
- Website
- waymo.com
Likely interview questions
- Describe a complex data warehouse or data lake project you built from conception to deployment and the challenges you overcame.
- How do you approach designing data models to balance performance, scalability, and maintainability?