Contribute to building a leading-edge transportation network at Amazon, enhancing customer delivery promises and advancing sustainability goals. This role offers the opportunity to develop critical data infrastructure that shapes the design of one of the world's most intricate logistics networks.

Join the Network Design Planning & Engineering (NDPE) team within Global Transportation Services (GTS) and drive the creation of data engineering solutions. These solutions will empower model development and propose innovative designs to reduce costs while accelerating customer delivery speeds.

Design and deploy robust data pipelines tailored for various network design initiatives. Develop core data infrastructure to support the faster and automated execution of network design models, ensuring seamless end-to-end data flows.

Identify key business needs for design scenario analysis, establish data architecture strategies, and build an intuitive platform for program managers to conduct "what-if" analyses. Define data engineering processes utilizing AWS services to optimize accessibility, redundancy, data integrity, cost-efficiency, and storage.

Deliver high-quality solutions that accelerate product and model development for business needs, ensuring scalability and foresight for future requirements. Collaborate with cross-functional teams including Scientists, Engineers, Analysts, and Product Managers to grasp business and technical requirements, making informed decisions for program success.

A minimum of 3 years of experience in data engineering is essential. Proficiency in SQL is required, with at least 4 years of experience. Demonstrated experience in data modeling, data warehousing, and constructing ETL pipelines is necessary.

Strong coding abilities, preferably in Python, are vital for leading technical design and ensuring efficient code deployment and maintenance. Familiarity with AWS technologies such as Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles is highly advantageous. Experience with diverse data storage solutions, including non-relational databases like object, document, key-value, graph, and column-family stores, is preferred.