Job 175 van 426

Amazon Europe Core


Business Intelligence Engineer


Business Intelligence Engineer, EU GTS Network Design Analytics

Have you ever wondered how to optimize one of the most complex logistics networks in the world? How about how products on Amazon websites get delivered so fast, and what kinds of algorithms and processes are running behind the scenes? Are you a data driven individual, interested in optimization, explainability and like to solve problems? If so, this role if for you!

Amazon's EU Global Transportation Services Network Design Analytics team is seeking a skilled Business Intelligence Engineer. Your role will focus on solving complex problems that impact our organization, particularly in developing the logic that powers robust tools. You will be at the forefront of Network Design, connecting scientific methods with data engineering practices to business, working on advanced analytical solutions for optimizing Amazon's transportation network. You will leverage strong technical skills to drive better models and simulations for Network Design.


We are looking for you:

  • You like to solve problems and have a technical curiosity
  • Are driven and have a strong interest in understanding how things work and how to improve them
  • Have immense attention to detail, creativity and strong initiative
  • Are committed to high standards in code, analytics and deliverables
  • Have persistent ownership: you are proactive and bring your own ideas to the table, you collaborate with your peers and team to deliver results
  • You want to engage with various stakeholders inside the company


Key job responsibilities

  • Drives and owns the technical aspects of network optimization programs
  • Architects and automates ETL pipelines for design tools
  • Leverages AWS tools and build systems to ensure solutions are automated and robust
  • Leads strategies in optimizing network design
  • Works at the intersection of tech and business, leveraging advanced tech skills with strong domain knowledge
  • Spearheads deep dives into hypotheses and anecdotes, taking on peculiar challenges of the Amazon network
  • Brings strong insights and solutions to the table
  • Productionalizes models end to end for business use with real life impact

A day in the life
You will typically engage in a variety of activities, starting from sprint planning, which will lead to analyses, developing tool logic and coding, building and running models and simulations and driving projects with both tech and non-tech stakeholders. You will participate in brainstorming sessions, code reviews and analyzing results, and may partake in higher visibility meetings to drive improvements and deliverables.
This will all roll up to impacting the transportation network with tangible, positive improvements for the customer.

About the team
Our team is a very dynamic technical team, where we value learning and development. We believe that learning by doing, "failing quickly" to improve quickly and having ownership are the best ways to develop knowledge and grow in your career. We are structured by functional skills and we partner and learn from one another. We maintain strong relationships with stakeholder teams by delivering work of high standards.

We are open to hiring candidates to work out of one of the following locations:
Luxembourg, LUX


Basic Qualifications

  • 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 in the data/BI space

Preferred Qualifications

  • 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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