Principal Data Engineer, Data Quality & Datasets Pipelines

Wayve is building the autonomous driving intelligence platform that will power vehicles of the future. The company uses an end-to-end AI approach that enables vehicles to learn directly from real-world driving experience, adapting and improving at scale without relying on hand-coded rules or pre-mapped environments. Wayve's mapless, hardware-agnostic platform integrates with global OEM partners to unlock advanced levels of automation from L2+ through to L4 capabilities.

As Principal Data Engineer for Data Quality and Datasets Pipelines, you'll lead the systems that manage Wayve's driving datasets as they scale from hundreds of petabytes toward exabyte levels. The Datasets team owns the complete lifecycle of driving data that trains the AI Driver—ingestion, annotation, retrieval, and quality assurance. You'll work on the Simulation, Evaluation, and Validation team to ensure datasets contain the behavioral and environmental coverage that model training needs, balancing data value against storage costs and compute efficiency.

What you'll do

• Design and implement filtering, retention, offload and deletion policies across hot, cold, archive and delete storage layers to optimize data lifecycle management

• Build the data movement systems and pipelines that support pre-ingestion selection models, deciding where and how these models execute while balancing computational cost against data value

• Create early, low-compute decision points that prevent expensive standardization and enrichment of low-value data, delivering measurable storage cost savings

• Collaborate closely with storage infrastructure and data teams to monitor data quality and ensure datasets reflect what model training actually requires

• Turn dataset strategy into operational systems managing hundreds of petabytes of driving data in production environments

What you'll bring

• Senior-level experience running data ingestion or data quality pipelines at petabyte scale

• Strong proficiency in Python and Spark with hands-on cloud data engineering experience

• Sound judgment in data filtering and quality decisions, with the ability to validate that valuable data hasn't been discarded

• A track record of shipping practical solutions quickly and iterating on live systems

• Software engineering expertise focused on building systems around models rather than the models themselves

• Comfort collaborating closely with data infrastructure and storage teams

Nice to have

• Background in autonomous vehicles, robotics, fleet data or high-volume IoT systems

• Experience designing retention, offload, or data lifecycle policies

What they offer

• Salaries benchmarked annually against the market, plus meaningful equity sharing in long-term company success

• Relocation support and visa sponsorship where applicable

• Hybrid working arrangement with core hours and hands-on access to vehicle workshops and labs

• Learning and development budgets supporting training, conferences and professional growth

• Comprehensive benefits including health insurance, dental, enhanced parental leave, retirement or pension plans, therapist access and wellness partnerships