Data Engineer, Data Quality & Provenance

Highlights

• Build trusted data foundations powering self-driving technology at petabyte scale.
• Transform fleet and simulation data into versioned datasets for training and safety.
• Combine real-world fleet logs with simulation for closed-loop development.

What they offer

• Market-benchmarked salaries reviewed annually
• Meaningful equity ownership in Wayve
• Relocation support where applicable
• Hybrid working with core hours and hands-on workshop access
• Learning and development budget for training and conferences
• Health insurance, dental, enhanced parental leave and pension
• Wellbeing support including therapists and team socials

Ideal candidate

A platform-minded data engineer who thrives in ambiguity and owns trusted, reproducible data products end-to-end. You care about data quality and enjoy partnering with autonomy and ML teams to enable safe self-driving.

Requirements

• Strong hands-on Python and SQL skills with solid production software-engineering fundamentals
• Designed and operated large-scale distributed data systems beyond small analytics pipelines
• Production experience with distributed processing and workflow orchestration such as Spark, Flyte or Airflow
• Built cloud-based data platforms using object storage with versioning, querying, governance and cost management
• Owned data quality, lineage, observability, reproducibility and incident response for production workflows
• Can translate ambiguous requirements from ML, data-science or robotics teams into reusable platform capabilities
• Comfortable working in ambiguity and defining standards for a growing data platform