Data Engineer, Data Quality & Provenance

Before the detail, here's the challenge you'd help us solve. We build the embodied intelligence that moves real vehicles safely, and the ecosystem a billion machines will run on in the future. Very few people in AI can say this. Every role here, whatever the team, plugs into that. Here’s what this particular role covers. 🛠️ About our Data Quality & Provenance Team Our Data Quality & Provenance team sits within Wayve’s AI Platform organisation and builds the trusted data foundations behind our self-driving technology. We transform vast volumes of fleet and simulation data into discoverable, reliable and reproducible datasets for model training, evaluation, replay, scenario mining and safety analysis. Operating at petabyte scale, the team defines the data products, standards and systems that enable our engineering teams to use data with confidence. 🧠 Your day-to-day Design and operate scalable batch and streaming pipelines for multimodal fleet and simulation data. Build data models, catalogues, indexes and query capabilities that make sensor, vehicle-state, map and event data easy to discover and use. Create versioned, reproducible datasets for training, evaluation, replay, scenario mining and safety analysis. Develop workflows for data ingestion, synchronisation, transformation, curation, labelling and quality validation. Partner with autonomy, ML, simulation and safety engineers to define schemas, APIs and data contracts. Improve the reliability, performance and cost efficiency of large-scale storage and compute workloads. 🧩 What you’ll be working on Petabyte-scale data products supporting autonomous-driving development across ML, autonomy, simulation and safety teams. Trusted datasets with strong versioning, lineage and reproducibility guarantees. Metadata, geospatial and temporal indexing that enables data discovery, selection and scenario mining. Systems that combine real-world fleet logs with simulation and synthetic data for closed-loop development. Data-quality standards, observability, access controls, retention policies and incident-response processes. Cloud-based storage and distributed-compute infrastructure designed for reliability, scalability and cost efficiency. 🙌 You should apply if You have strong hands-on Python and SQL skills, supported by solid production software-engineering fundamentals. You have designed and operated large-scale distributed data systems beyond small analytics or reporting pipelines. You have production experience with distributed-processing and workflow-orchestration technologies such as Spark, Flyte or Airflow. You have built cloud-based data platforms using object storage and understand data organisation, versioning, querying, governance and cost management. You have owned data quality, lineage, observability, reproducibility and incident response for production data workflows. You can translate ambiguous requirements from ML, data-science, robotics or similarly technical teams into durable, reusable platform capabilities. You are comfortable working in ambiguity and helping define the standards and operating model for a growing data platform. Experience with autonomous vehicles, ADAS, robotics, mapping, sensor-rich systems, geospatial data or multimodal datasets would be an advantage. 🌱 Not ticking every box? That’s totally okay! If you’re passionate about autonomy and keen to learn, we encourage you to apply even if you don’t meet every requirement.   More about Wayve: 🚀 Wayve is building the leading AI platform for autonomous driving. We are pioneering an end to end AI approach that enables vehicles to learn directly from real world experience, developing the ability to adapt, generalise and improve at scale. Instead of relying on hand coded rules or pre mapped environments, our AI Driver learns to drive by understanding the world around it. The result is technology that navigates complex urban environments with