Business Intelligence Engineer, Capacity Strategy and Insight Within The Amzl Eu Launch and Expansion Team

**DESCRIPTION**:
You will serve as a member of Capacity Strategy and Insight within the AMZL EU Launch and Expansion team, who are responsible for growing Amazon’s last mile delivery capability across Europe. Your focus will be on optimization, automation and scalability: improving the tools, reporting and insight we need to have in place to let us scale effectively over the next 3-6 years, and building the data automation and modelling that lets us do that: Where do we need better insight? What needs to be done to deliver that? How do we maintain the underlying data architecture to keep things scalable?
Scalability is also about the processes and mechanisms we use internally to manage and plan infrastructure and capacity, and you’ll be streamlining and improving those too. The automation and tools you create will have a big hand in making sure we have enough capacity to meet our customers’ needs across Europe over the coming years.

Key job responsibilities
- Assisting the team on managing a portfolio of analytics and modelling projects across multiple internal customers where demand invariably outstrips supply
- Act as design contributor for our data architecture, assisting the roadmap and making sure it meets the need of our present and future work
- Handle large volumes of data to optimize last-mile delivery operations. Create reports, analyse data, and derive actionable insights to drive decision-making
- Collaborate with matrixed, cross-functional teams to streamline the processes and mechanisms that let us plan and forecast accurately
- Build tools and automate mechanisms to let us deal with ever-increasing complexity at scale
- Utilize analytical skills, business judgment, and strategic thinking to innovate on behalf of customers.
- Own projects from inception to completion, growing our technical capabilities, and influencing work processes.
- Communicate clearly on development with technical and non-technical stakeholders throughout the whole project lifecycle.

A day in the life
As a Business Intelligence Engineer, you’ll be owning projects that are focused on providing insight and driving decisions that maximize our delivery capability whilst bringing cost down. You will use a combination of analytical skills, business judgment, and strategic thinking to innovate on behalf of our customers. Providing support and mentoring to develop the team. Your work will cover creating and improving reports, discovering new technical capabilities, writing documents, building tools, and helping influence the way that we work within L&E. You’ll be partnering with diverse cross-functional teams to develop new processes and mechanisms as our business evolves.

About the team
You click on your screen, and next day an Amazon parcel arrives at your doorstep. It must be magic, right? Well, not entirely. It’s a mixture of precision, invention, and world class supply chain logistics. It’s a host of teams working closely together to deliver a slick experience for our customers. It’s the use of data to understand what’s going to be needed and making sure we’re ready to handle the demand. It’s paying attention to the detail whilst also having a really good understanding of the big picture. And this role plays a big part in making it happen.

**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