Senior Data Engineer - Autonomous Driving Data Pipelines

inDrive; Abu Dhabi; Sur place; AED 300 000 - 420 000; Plein temps

Résumé du poste

inDrive is building the data foundation for autonomous driving and seeks a data engineering lead to own multi-sensor data pipelines. You will manage imagery, GPS tracks, IMU/LiDAR streams, and storage, ensuring synchronization and geo-reference in cloud storage, with cost-aware data lifecycle and validation.

You will collaborate globally, work with BigQuery, PostGIS, and GeoParquet, and mentor teams while improving data quality and performance for ML training datasets across 47 countries.

Qualifications

• 4 years in data engineering
• Experience with multi-sensor data from vehicles or robots (cameras, IMU or LiDAR) and keeping 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) with distributed processing (Spark or equivalent)
• Hands-on experience with spatial data: PostGIS, GeoPandas/Shapely, coordinate reference systems, GPS tracks

Responsabilités

• Build and maintain data pipelines ingesting multi-sensor data from vehicles into cloud storage
• Ensure data streams are synchronized and geo-referenced
• Prepare training datasets for autonomous-driving models and deliver in ML-ready formats
• Collaborate with global teams and optimize data storage costs

Connaissances

Python

SQL

AWS

GCP

Docker

Kubernetes

BigQuery

PostGIS

GeoParquet

Spark

OpenStreetMap

Outils

Airflow

Databricks

OpenTelemetry

Description du poste

inDrive is building the data foundation for autonomous driving and seeks a data engineering lead to own multi-sensor data pipelines. You will manage imagery, GPS tracks, IMU/LiDAR streams, and storage, ensuring synchronization and geo-reference in cloud storage, with cost-aware data lifecycle and validation.

You will collaborate globally, work with BigQuery, PostGIS, and GeoParquet, and mentor teams while improving data quality and performance for ML training datasets across 47 countries.