Amazon
Location
Madrid | Spain
Job description
The Supply Chain Optimization Technologies (SCOT) organization owns Amazon’s global inventory management systems: we build systems that decide what, when, where, and how much we should buy to support Amazon’s business and to make our customers happy. We do this for millions of items, for hundreds of product lines worth billions of dollars of inventory world-wide. Our systems are built entirely in-house, and are on the cutting edge in automated large-scale business, inventory and supply chain planning and optimization systems. We foster new game-changing ideas, creating ever more intelligent and self-learning systems to maximize the efficiency of Amazon's inventory investment and placement decisions.
The Automated Inventory Management team (AIM) within SCOT seeks an experienced and motivated Business Intelligence Engineer to develop analytical models and tools to automate the auditing of the SCOT systems. Such tools may include algorithms, metric bridges, dashboards, processes and workflow systems. The successful candidate will have strong quantitative data mining and modeling skills and be comfortable working on new and highly ambiguous problems from concept through to execution. They will have strong communication and leadership skills, will be able to collaborate with other teams (e.g. software development, business owners, product managers) and to present findings to senior audiences to drive business improvements. Responsibilities include:- - 3+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc. experience
- - Experience with data visualization using Tableau, Quicksight, or similar tools
- - Experience with data modeling, warehousing and building ETL pipelines
- - Experience in Statistical Analysis packages
- - Experience using SQL to pull data from a database or data warehouse and scripting experience (Python) to process data for modeling
- - Experience with AWS solutions such as S3, and Redshift
- - Experience in data mining, ETL, etc. and using databases in a business environment with large-scale, complex datasets
Job tags
Salary