Wayve
Data Engineer, Security Analytics
SunnyvalemidAdded today
About this role
Wayve is seeking a Data Engineer to build the company's security analytics capability from the ground up. You'll design scalable data pipelines, automate evidence collection, and create a data-driven security program that transforms security data into actionable insights for decision-making.
What you'll do
- Design and maintain scalable data pipelines that collect, transform, and enrich security data from across the business
- Integrate data from identity, cloud, engineering, and security platforms to create a unified security posture view
- Build robust data models and analytics products that measure control effectiveness and security program health
- Develop automated evidence collection capabilities to reduce manual effort in governance and security operations
- Create meaningful KPIs and metrics that support data-driven security decision-making
- Ensure data quality, lineage, reliability, observability, and maintainability across the platform
What they're looking for
- Data pipeline design and implementation (ETL/ELT)
- Python or SQL for production data solutions
- Data modeling for analytics
- Cloud-native environments and SaaS integrations
- API-based integrations and automation
- Data quality and observability practices
- Analytical and problem-solving abilities
- Security domain knowledge (compliance, controls, governance)
Opens the official application on the employer’s site. No login required.
Wayve
Wayve develops autonomous driving AI technology and systems for vehicles. The company is hiring for validation engineers, systems engineers, ML engineers, integration specialists, and program managers to build and deploy autonomous driving platforms across testing, AI development, hardware integration, and production scaling.
- Website
- wayve.ai
Likely interview questions
- Walk us through your experience designing and operating a production data platform—what was the biggest challenge you faced?
- Describe a time you built automated evidence collection or reporting. How did you ensure data quality and reliability?