Job Description

DESCRIPTION

Are you passionate about making the world a better place, and doing so by leveraging technology and data at scale to innovate and drive better decision making?

The Sustainability Science and Innovations (SSI) team is looking for an experienced Business Intelligence Engineer to join our team in building customer-focused sustainability products. SSI is an interdisciplinary team of Scientists, Engineers, and Economists incubating and building Day One sustainability solutions using cutting-edge technology, to solve some of the toughest business problems at Amazon.

At Amazon, we work hard to ensure that every Amazon product or service our customers purchase is made or handled in a way that ensures respect for the rights of workers throughout our supply chain and operations. Our Sustainability/Social Responsibility team partners with internal businesses, suppliers, and service providers on a continuous improvement model, researching and identifying risks in places where we source and operate, and designing solutions to help understand and mitigate those risks.

As an SSI business intelligence engineer supporting Social Responsibility, the ideal candidate will lead complex data analysis to identify factors related to working conditions at factories and service providers. They are intellectually curious, highly self-directed, exhibit a strong attention to detail, are well-organized, can manage multiple projects simultaneously, and deliver results in a fast-paced and ambiguous environment. They will turn research into actionable business intelligence that presents recommendations for senior leaders and socialize these findings to teams across Amazon.

This is a unique opportunity for someone who wants to combine their passion for sustainability, solving customer problems using data, and working on a high-performing team that includes many world-leading experts in their fields. In this role, you will partner closely with scientists and other BIEs to build products that enable businesses across Amazon to incorporate sustainability impacts into their decision-making. You will truly be on the cutting edge in one of the most important fields in the world!

The successful candidate will work closely with partners within Social Responsibility as well as stakeholders around the company to scale data-driven operations, generate insights, and provide ad-hoc leadership support in this evolving space. You will dive deep into datasets that span all of Amazon's businesses, with an expectation to produce the best analytics and metrics to solve problems. We are looking for a candidate with solid analytical skills, the ability to work independently, manage competing priorities in a fast-paced environment, a strong bias for action, superb customer focus, and the ability to communicate with and influence business groups.
BASIC QUALIFICATIONS

Proficiency with SQL
Experience with at least one modern scripting language (e.g. Python)
Experience working directly with business stakeholders to translate between data and business needs
Impeccable attention to detail and ability to check your own work
Strong verbal/written communication & data presentation skills, including an ability to effectively communicate with business leaders and technical teams
Be self-driven, and show ability to deliver on ambiguous projects with incomplete or dirty data
Experience with data visualization using Tableau, Quicksight, or similar tools
Bachelor's Degree in Statistics, Applied Mathematics, Econometrics, Computer Science, Operations Research, Engineering, closely related field or equivalent experience.
4+ years of experience as an analyst or engineer in the data/BI space

PREFERRED QUALIFICATIONS

Proven ability to solve complex quantitative business challenges
Proficiency with at least one modern scripting language (e.g. Python)
Masters degree or foreign equivalent in Computer Science, Engineering, Mathematics, Statistics, Economics, or a related field
Experience working with AWS big data technologies (EMR, S3, Kinesis, Redshift)
Experience with statistical modeling, regression, and machine learning algorithms