Data Engineer

The Role

We're looking for a Data Engineer to join our Data Engineering team and play a pivotal role in shaping how Tesla leverages data across its Sales, Delivery, and Service lifecycle. You'll design, build, and scale our Enterprise Data Warehouse and AI/BI-powered intelligent solutions — directly impacting how the entire organization makes decisions, optimizes operations, and delivers on Tesla's mission.

This is a high-visibility role with the opportunity to work at the intersection of large-scale data engineering and applied AI, building systems that serve teams and factories across the globe.

Responsibilities：

Architect & Deliver enterprise-grade Data Warehouse solutions in fast-paced, deadline-driven environments.

Build & Optimize ETL/ELT pipelines at scale using Spark, Flink, and related technologies.

Design Real-Time Data Infrastructure — implement streaming and event-driven architectures using Kafka, Spark Streaming, and similar frameworks.

Develop AI-Powered Data Agents that interface with LLMs, data warehouses, and BI platforms — engineered to handle high-throughput traffic with reliability and low latency.

Standardize & Scale BI Solutions across APAC operations, ensuring consistency and reusability across regions.

Translate Business Challenges into Technical Solutions — partner closely with stakeholders to decompose complex pain points into well-scoped, executable engineering deliverables.

Establish Robust Automation — build scalable, repeatable processes for data analysis, model development, validation, and deployment.

Collaborate Cross-Functionally with business sponsors and IT teams to prioritize, estimate, and deliver enhancements — especially under time-critical conditions.

Requirements

Must Qualifications

Proven experience designing and operating large-scale data pipelines in production environments.

Strong expertise in Data Warehouse ETL/ELT design, development methodologies, tooling, and best practices.

Hands-on experience with AI agent development, including working knowledge of coding agents, LLM integration patterns, LLM Wiki, and tool/skill orchestration.

Preferred Qualifications

3+ years of hands-on development experience with technologies such as Hive, Hadoop, StarRocks, Spark, and/or Flink.

Deep experience with data warehouse architecture and data middle platform design, development, and governance.

Genuine passion for applying AI/ML to real-world engineering problems — you don't just use the tools, you push their boundaries.

Familiarity with high-concurrency system design and performance optimization for analytics platforms.

Strong communication skills with the ability to operate effectively across technical and non-technical audiences.