Join Amazon Autos as a Sr. AI Platform Data Engineer, working at the dynamic intersection of data engineering and data science. This role involves building cutting-edge platforms and developing Agentic Artificial Intelligence (AI) solutions to drive marketplace innovation. You will architect and implement platform features, along with creating curated datasets essential for self-service analytics and intelligent automation.

This unique opportunity places you at the forefront of AI-powered data engineering. You will not only utilize advanced AI tools but also contribute to building the next generation of AI-enabled data platforms. Experience the autonomy of owning projects from inception to completion while significantly shaping the future of automotive shopping on Amazon.

Key responsibilities include developing and maintaining efficient, scalable data pipelines and curated datasets optimized for AI/ML. You'll implement systems for automated data classification, transforming business challenges into analytical solutions, and build AI-native self-service data platforms using an AI-first methodology. Additionally, you will create AI-powered platforms with natural language interfaces for enhanced data access.

Serve as a key technical partner to business stakeholders, data science teams, and cross-functional collaborators. Deliver well-modeled, discoverable data tailored for specific use cases. Utilize AI code generation and Agentic AI tools to design and build curated datasets. Effectively communicate insights and recommendations to product, business, and technology partners.

This role requires a minimum of 4 years of dedicated data engineering experience, with a strong foundation in data modeling, warehousing, and building robust ETL pipelines. Proficiency in SQL is essential, alongside experience in at least one modern scripting or programming language such as Python, Java, Scala, or NodeJS. The ideal candidate will also have experience mentoring team members on best practices. Experience with big data technologies like Hadoop, Hive, Spark, or EMR, and operating large data warehouses, are considered preferred qualifications.