Senior Data Engineer (AV)
You will build and maintain end-to-end pipelines that ingest multi-sensor data from autonomous vehicles into cloud storage, ensuring synchronization and geo-referencing. You will also design the storage architecture and lifecycle policies to manage growing volumes cost-effectively.
Skills required
About this role
About the role
We are preparing the data foundation for our autonomous-driving work, and we are hiring the engineer who will start it. Today we collect street-level imagery and GPS tracks from vehicle cameras, including our own camera units with NVIDIA edge devices on board, and use them to update maps. Next comes a dedicated survey vehicle with a full sensor set: multi-camera, IMU, LiDAR. You will start with the imagery and map data pipelines we run today, and as the sensor platform arrives, you will build the pipelines that turn its data into training datasets for autonomous-driving models.
Key Responsibilities
- Build the pipelines that move multi-sensor data (multi-camera video, IMU, LiDAR, GPS) from vehicles into cloud storage, synchronize and geo-reference the streams
- Own the storage layout and lifecycle for sensor data: partitioning, hot and archival tiers, cost control as volume grows
- Build validation and monitoring on the ingest path: incomplete uploads, missing or unsynchronized streams, vehicles that stopped producing usable data
- Prepare training datasets for autonomous-driving models: select data by location, route, and conditions, version datasets, and deliver them in the formats ML teams need
- Until then: take over and develop the current imagery and GPS-track pipelines from vehicle cameras, and contribute to geodata pipelines (ingestion from company data, OpenStreetMap, Overture Maps and vendors, conflation, validation, publication)
- Work with company data in BigQuery and keep spatial queries fast on large PostGIS and GeoParquet tables
Skills, Knowledge & Expertise
- 4+ years in data engineering
- Experience with multi-sensor data from vehicles or robots: cameras, IMU or LiDAR, and keeping the streams in sync
- Experience preparing training datasets for computer vision or autonomous-driving models.
- Large volumes of binary or media data in object storage (S3 or GCS); distributed processing (Spark or equivalent)
- Hands-on experience with spatial data: PostGIS, GeoPandas/Shapely, coordinate reference systems, GPS tracks
- Strong Python and SQL; AWS or GCP, Docker, Kubernetes
Nice to have
- Sensor data formats such as ROS2 bag, MCAP or point clouds
- Parquet, Delta or Iceberg; Databricks
- Argo Workflows or Airflow
- OpenStreetMap and Overture Map
- QGIS
- BigQuery
