DESCRIPTION

The Amazon Environmental Systems and Data Services (ESDS) team is looking for an outstanding, analytical and technically skilled Business Intelligence Engineer to join our team. The ESDS team, part of the Product Assurance, Risk and Security (PARs) organization, manages the systems, data, and automation of Environmental programs globally. The ESDS team, develops, implements, and tracks programs globally through data analysis and enables the implementation of these programs by developing scalable systems, leveraging data to measure performance, gain insights, and identify actionable improvement opportunities.

This position will be responsible for building and supporting the business analytics platform that supports the Environmental Assurance and Protection (EAP) team. The incumbent will be responsible for designing and supporting best-in-class data infrastructure and reporting solutions to aid Environmental and Operations team members in decision making. They will continue to raise the bar for the business analytics function and reporting framework.

This role requires an individual with excellent statistical and analytical abilities, deep knowledge of business intelligence solutions and data engineering practices as well as outstanding business acumen and ability to work with various teams across Amazon. The successful candidate will be a self-starter comfortable with ambiguity, with strong attention to detail, an ability to work in a fast-paced and ever-changing environment, and driven by a desire to innovate.

Responsibilities
You know and love working with business intelligence tools, can model multidimensional datasets, and can partner with customers to answer key business questions. You are analytical and creative, and you don’t quit. You will also have the opportunity to display your skills in the following areas:



· Architect, design, implement, and support a platform providing secured access to large datasets.
· Interface with technical and non-technical customers delivering complete BI solutions.
· Model data and metadata to support ad-hoc and pre-built reporting.
· Own the design, development, and maintenance of ongoing metrics, reports, analyses, dashboards, etc. to drive key business decisions.
· Recognize and adopt best practices in reporting and analysis: data integrity, test design, analysis, validation, and documentation.
· Analyze and solve problems at their root, stepping back to understand the broader context.
· Use data mining, model building, and other analytical techniques to develop and maintain customer segmentation and predictive models to drive the business.
· Respond to high priority requests from senior business leaders.
· Learn and understand a broad range of Amazon’s data resources and know when, how, and which to use and which not to use.
· Continually improve ongoing reporting and analysis processes, automating or simplifying self-service support for secondary datasets

BASIC QUALIFICATIONS

· Bachelor’s degree in Computer Science, Engineering, Math, Finance, Statistics or related work experience.
· 5+ years of relevant experience in business intelligence role, including data warehousing and business intelligence tools, as well as experience in diving deep on data analysis or technical issues
· 3+ years of professional SQL experience
· 2+ years of relevant work experience in a modern programming language (Preferably Python)
· Experience conceptualizing and implementing data warehousing or data lake solutions to support long term business data needs (familiarity with AWS, API’s, Amazon’s internal DW tool suite, etc.)
· Experience in data mining, ETL, etc. and using databases in a business environment with large-scale, complex datasets.
· Knowledge and direct experience using data visualization tools like QuickSight, Tableau.

PREFERRED QUALIFICATIONS

· Master’s degree in Computer Science, Math, Engineering, Economics, Finance, Statistics or a related discipline
· Experience working with AWS big data technologies (EMR, S3, Kinesis, Redshift)
· Proven ability at looking at solutions in unconventional ways. Sees opportunities to innovate and can lead the way.
· Excellent communication (verbal and written) and interpersonal skills to convey key insights from complex analysis in summarized business terms and an ability to effectively communicate with technical and non-technical teams.
· Ability to deliver on ambiguous projects and work with complex datasets
· Impeccable attention to detail and ability to check your own work
· Experience with statistical modeling, regression and machine learning algorithms
· Knowledge of data science libraries such as scikit-learn and pandas