
Overview
- 3+ years of data engineering experience - 4+ years of SQL experience - Experience with data modeling, warehousing and building ETL pipelines
SCOT's Automated Inventory Management (AIM) team seeks talented individuals passionate about solving complex problems and driving impactful business decisions for our executives. The AIM team owns critical Tier 1 metrics for Amazon Retail stores, providing key insights to improve store health monitoring. We focus on enhancing selection, product availability, inventory efficiency, and inventory readiness to fulfill customer orders (FastTrack) while enabling accelerated delivery Speed & Fulfillment Worldwide. This improves both Customer Experience (CX) and Long-Term Free Cash Flow (LTFCF) outcomes. Our approach involves creating standardized, scalable, and automated systems and tools to identify and reduce supply chain defects in our systems and inputs, while driving operational leverage and scaling. As a Data Engineer, you will analyze large-scale business data, solve real-world problems, and develop metrics and business cases to delight our customers worldwide. You will work closely with Scientists, Engineers, and Product Managers to build scalable, high-impact products, architect data pipelines, and transform data into actionable insights to manage business at scale. We are looking for people who are motivated by thinking big, moving fast, and exploring business insights. If you love to implement solutions to hard problems while working hard, having fun, and making history, this may be the opportunity for you. About the team Supply Chain Optimization Technologies (SCOT) is the name of a complex group of systems designed to make the best decisions when it comes to forecasting, buying, placing, and shipping inventory. Functionally these teams work together to drive in-stock, drive placement, drive inventory removal and manage the customer experience.
- Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
- Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, column-family databases)
- Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
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