Backend Team Lead - REM Engineering

As the Backend Team Lead for REM Ingestion, you will spearhead the design, development, and scaling of the critical entry point for Mobileye’s Road Experience Management (REM) platform. Your team is responsible for capturing, processing, and validating massive streams of crowdsourced data from millions of vehicles worldwide in real time.This is a hybrid leadership and technical role. You will lead a squad of engineers while remaining hands-on in architecting resilient, ultra-high-throughput distributed systems that power the future of autonomous driving and advanced mapping.

what will your job look like:

• Drive Execution: Balance hands-on development (coding, code reviews, and architecture) with project management, resource planning and delivery.
• Architect at Scale: End-to-end architecture of ingestion pipelines capable of handling massive scale, ensuring low latency, high availability, and optimal cost-efficiency.
• Lead & Mentor: Empower, guide, and scale a talented team of backend engineers. Foster a culture of technical excellence and continuous learning.
• Optimize Infrastructure: Continuously improve system performance, data throughput, and cloud infrastructure utilization (compute, storage, and networking costs).
  
  

• 2+ years of experience leading, mentoring, and growing backend engineering teams in a fast-paced environment.
• 5+ years of deep backend experience building high-scale, distributed cloud architectures.
• Hands-on experience with high-throughput streaming and queuing systems (e.g., Apache Kafka, AWS Kinesis, RabbitMQ, or equivalent)
• Strong experience with cloud providers, preferably AWS (core services like EKS, S3, CloudFront) and containerized workflows (Docker, Kubernetes)
• Proficiency in Node.js and/or Python
• Solid understanding of high performance relational/NoSQL databases
• Experience with task queues and stream processing frameworks (Celery, Spark etc.)-Advantage
• Background in geospatial data processing or handling- Advantage
• Familiarity with Data Lake architectures (e.g., Iceberg, Glue, or similar high-scale storage formats).