Amazon Extra Large (AMXL) is looking for a customer focused, analytically and technically skilled Business Intelligence Engineer to drive effective performance measurement and management, innovation and execution in last mile delivery. This position will be responsible for building out analysis and visualization tools and processes to support our growing Amazon Logistics business in Europe.

The successful candidate will be owning part of the infrastructure and helps internal AMXL teams

The Business Intelligence Engineer II (BIE II), is the single point of contact for the AMXL BI Data Engineering (DE) Initiatives.

This person is responsible for (1) managing intake, coordinating and gathering requirements from business partners from EU7 AMXL Network. With expansion managing an increase of data volume, and the need for more cost-efficient and scalable pipelines and tech solutions. (2) Prioritizing initiatives, (3) Design and implement technical solutions with an appropriate strategy and data set design, and (4) Drive best practices in Operational Excellence.

The successful candidate will be able to effectively retrieve, integrate, visualize and present critical data to Senior Leaders. This individual will work with technical and business teams to optimize planning, execution, process improvement and track progress against goals.

Key job responsibilities
• Partner with internal stakeholders across multiple teams, gathering requirements and delivering complete solutions.
• Partner with BIEs and Data Engineering teams to prioritize and define AMXL data and BI development needs.
• Work with in-house scientists, global supply chain, transportation and logistics teams to identify new BI capabilities and projects.
• Own a share of admin responsibilities for the team's Analytics Solution (Redshift cluster, Quicksight account, etc.).
• Drive key tech strategical projects for the EU AMXL team.
• Develop queries and visualizations for both ad-hoc requests and long term projects, as well as recurrent business review reporting.- Experience in analyzing and interpreting data with Redshift, Oracle, NoSQL etc.
- Experience with data visualization using Tableau, Quicksight, or similar tools
- Experience with data modeling, warehousing and building ETL pipelines
- Experience in Statistical Analysis packages such as R, SAS and Matlab
- Experience using SQL to pull data from a database or data warehouse and scripting experience (Python) to process data for modeling
- Experience with SQL- Experience with AWS solutions such as EC2, DynamoDB, S3, and Redshift
- Experience in data mining, ETL, etc. and using databases in a business environment with large-scale, complex datasets

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