Data Engineer

Pipeline Development: Design, build, and maintain highly scalable and fault-tolerant data pipelines to ingest, process, and store real-time IoT and EV telemetry data (CAN logs, BMS readouts). Architecture Ownership: Lead the design and optimization of the data lake and data warehouse architectures, ensuring low-latency access for downstream AI models and diagnostic dashboards. AI Integration: Work closely with AI developers and full-stack engineering teams to ensure data models support advanced diagnostic algorithms and real-time frontend telemetry rendering. Cloud & Containerization: Deploy and manage data infrastructure using Docker and cloud environments (Azure/AWS), ensuring high availability and cost-efficiency. Core Languages: Expert-level proficiency in Python and advanced, highly optimized SQL. Big Data & Processing: Deep experience with distributed data processing engines (e.g., Apache Spark, Flink, or Kafka for streaming data). Modern Data Stack: Strong hands-on experience with modern cloud data ecosystems, OLAP databases, and table formats (e.g., Apache Iceberg, ClickHouse, Trino/Presto). Orchestration & Transformation: Proven expertise using Apache Airflow for pipeline orchestration and dbt for data transformation. DevOps & Infrastructure: Solid understanding of Docker containerization, CI/CD pipelines, and cloud administration (Azure/AWS).