The Business Intelligence Engineer position will play a critical role in identifying and supporting the strategic initiatives of the Last mile division. The potential candidate will partner with stakeholders and program managers to create metrics, reports, dashboards and automated mechanisms to drive continuous improvement with actionable insights.
As an Amazon Business Intelligence Engineer you will be working in one of the world’s largest and most complex data warehouse environments. You should have deep expertise in the design, creation, management of extremely large datasets. You should have excellent business and communication skills to be able to work with business owners to develop and define key business questions, and to build data sets that answer those questions. You should be expert at designing, implementing, and operating stable, scalable, low-cost solutions to flow data from production systems into the data warehouse and into end-user facing applications. The potential candidate will have opportunities to explore and utilize generative BI and machine learning solutions to innovate and find most efficient solution for complex business problems.
Above all you should be passionate about bringing large datasets together to answer business questions and drive change.
Key job responsibilities
* Partner with internal stakeholders and program managers across multiple teams, gathering requirements and delivering end to end business intelligence solutions.
* Provide timely and actionable insights into current business performance. This will require data gathering and manipulation, synthesis and modeling, problem solving, and communication of insights and recommendations.
* Analyze and visualize process adherence and performance data to articulate user behavior or process problems to support successful execution of the programs.
* Partner with data engineers to identify the source of data and create a data pipeline to collect necessary business inputs to deliver the analytical product.
* Support pilot studies by building robust pilot analyses plan, data collection processes, and analyzing the output to deliver final recommendations.
* Utilize predictive analytics, machine learning and generative BI tools to continuously improve the quality of the analytics products and effectiveness of the business insights
* Develop queries and visualizations for ad-hoc requests and projects, as well as ongoing reporting.
A day in the life
You will be working on Redshift, Quicksight, Python to pull, manipulate and present data in dashboards. You will be working with stakeholders to understand analytic business requirements and build dashboards for use in the AMZL network. You will have the opportunity to do basic machine learning models like Clustering, regression required for benchmarking products.
About the team
This role is within Amazon Last Mile division (AMZL). AMZL manages the delivery of millions of packages to customers through Delivery Stations. The Global Benchmarking, Standards and Audit (GBSA) team within AMZL performs benchmarking analysis to identify best-in-class performances and highlight areas for further analysis. Findings from these analysis feed our Standards Improvement activities on a global scale. Adoption of the standards we create will be measured through a global audit program that creates additional data points for benchmarking analysis, and provides a mechanism for local operators to address gaps to proven standard work.
BASIC QUALIFICATIONS
1) 1+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc. experience
2) Experience with data visualization using Tableau, Quicksight, or similar tools
3) Experience with one or more industry analytics visualization tools (e.g. Excel, Tableau, QuickSight, MicroStrategy, PowerBI) and statistical methods (e.g. t-test, Chi-squared)
4) Experience with scripting language (e.g., Python, Java, or R)
PREFERRED QUALIFICATIONS
1) Master’s degree, or Advanced technical degree
2) Knowledge of data modeling and data pipeline design
3) Experience with statistical analysis, correlation analysis
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