The EU Workforce Staffing Analytics team is responsible for our hourly workforce : Who do we hire? How should we hire them?
How do we make sure the right candidate gets the right role at the right time, and how do we do this scalable & efficiently?
As Amazon moves toward free one day shipping for prime customers, and as we continue to scale, answering these questions will have material impact on the business, and on the experience our candidates have.
Here’s where you come in :
We are looking for a talented EU WFS Business Intel Engineer to help manage our ever-growing data & reporting needs and support analytics of an increasingly complex business related to Workforce Staffing (WFS) across the EU.
As a Business Intelligence Engineer, you will generate insights that will guide operational excellence and business strategy for our customers.
Data analysis is at the core Amazon’s culture, and your work will have a direct impact on decision making and strategy for our organization.
You will be gathering customer insights, mining data, making recommendations, and helping senior leaders make key business decisions.
You will have the opportunity to work with large and diverse data sets to gather insights using data from across Amazon Operations.
The ideal candidates will have excellent analytical abilities, outstanding business acumen and judgment, intense curiosity, strong technical skills, and superior written and verbal communication skills.
They will have a strong bias toward data driven decision making. They will be a self-starter, comfortable with ambiguity, able to think big and be creative (while paying careful attention to detail).
Enjoys working in a fast-paced dynamic environment. If you are excited about data, are results oriented, and want to join a growing analytics team within Amazon, this role is for you.
Main Responsibilities :
Be part of a high performing team of Business Intelligence Engineers
Constantly challenging the status quo and proposing alternative solutions to drive the efficiency
Design, develop and maintain scaled, automated, user-friendly systems, reports, dashboards, etc. that will support the needs of the business.
Apply deep analytic and business intelligence skills to extract meaningful insights and learning from large and complicated data sets.
Be hands-on with ETL to build data pipeline to support automated reporting.
Serve as liaison between the business and technical teams to achieve the goal of providing actionable insights into current business performance, and ad hoc investigations to support future improvements or innovations.
This will require data gathering and manipulation, problem solving, and communication of insights and recommendations.
Build various data visualizations to tell the story of business trends, patterns, and outliers through rich visualizations.
Recognize and adopt best practices in reporting and analysis : data integrity, test design, analysis, validation, and documentation.
Ability to travel; 10%+ travel expected
Bachelor’s or Master's degree in Computer Science, Statistics, Mathematics, Economics or a related discipline
3+ years of relevant work experience in data science, business analytics, business intelligence (BI), or comparable experience in big data environments
3+ years of experience with Amazon QuickSight, Tableau Desktop or other relevant data visualization software
3+ years of experience in data mining and / or data-set preparation using SQL
Knowledge of data warehouse technical architecture, infrastructure components, ETL and reporting / analytic tools and environment
Be self-driven, details-oriented, and show ability to deliver on ambiguous projects with incomplete or dirty data.
Proficient with Python or other relevant scripting language
5+ years of experience in a BIE role with a technology company.
Strong verbal / written communication and data presentation skills, including an ability to effectively communicate with both business and technical team, and senior management as required.
Familiarity with Amazon's AWS services including Glue, EC2, S3 and Redshift.
Experience working in large data warehouse environments