Do you want to play a crucial role in the future of Amazon's retail business by building data analysis systems that enable data driven decision-making at the largest scale possible?

The Promise team sets the expectation of when customers’ items will arrive. The delivery promises we make are key drivers of customer behavior and influence the short and long-term profitability of Amazon’s global retail business. We serve as the interface between customer expectations and Amazon's industry-leading fulfillment capabilities. We are part of the Supply Chain Optimization Technology (SCOT) Group that develops and manages systems that optimize inventory acquisition and placement, so products are available for fast delivery. (For more information on SCOT, see this short video: Successful members of the Promise team are customer obsessed, flexible, and collaborative team players who enjoy working across functions and organizations to solve problems and get results. They ask hard questions and build solutions that provide the critical business insights needed to influence decision-making across retail and operations teams. They find timely answers buried in large data sets and complex systems, identify root causes, and get their hands dirty building data systems and sharing insight.

The Promise team is seeking a Business Intelligence Engineer with broad technical and analytical skills to develop the data analysis systems we use to make decisions to optimize delivery promises for Amazon's customers worldwide. As an Amazon Business Intelligence Engineer, you will be working in one of the world's largest and most complex data warehouse environments. You will work on analytics, visualization solutions, and other Business Intelligence applications used by researchers, engineers, and management across multiple departments. You should be reliable at designing, implementing, and operating stable, scalable, low-cost solutions that analyze data from production systems into end-user facing applications. You should have deep expertise in the design, creation, management, and business use of extremely large datasets. You will need excellent business and communication skills to work with researchers and business owners in a fast-paced environment to develop and define key business questions and requirements. Above all, you should be passionate about working with huge datasets and analytics tools to provide insight into how the business is running and enable business decisions to be data driven.

Key job responsibilities
· Defining, developing and maintaining critical business and operational reports reviewed on a weekly, monthly, quarterly, and annual basis
· Transform complicated business problems into mathematics modeling and provide data-driven solutions
· Analysis of historical data to identify trends and support decision-making, including written and verbal presentation of results and recommendations
· Collaborating with software development teams to implement analytics systems and data structures to support large-scale data analysis and delivery of machine learning and econometric models
· Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation, and model implementation.
· Mining and manipulating data from database tables, simulation results and log files
· Identifying data needs and driving data quality improvement projects
· Understanding the broad range of Amazon’s data resources, which to use, how, and when.

A day in the life
· Routinely performs and presents deep dives and anecdotes to gain more granular understanding of why a promise occurred and whether there are gaps in fulfillment systems which, if resolved, could speed up delivery. Build logic to attribute these root causes at large scale and put together underlying data pipeline to make this information available for metricizing and accessible for other teams within Amazon.
· Regularly reviews the promise metrics for putting together callouts for weekly business reviews by performing ad hoc analysis and reaching out to appropriate teams to get further understanding of the issue. Another part of this exercise is to track and callout negative trends which need more attention from the leadership, identify the root cause(s), and recommend next steps to resolve the issue.
· Works cross-functionally with various teams in Fulfillment Optimization organization to understand how they interact and affect the order fulfillment process and identify system gaps which could impact promise speed. They continue to resolve the gap by working closely with software teams to design and implement solutions to address these gaps and develop metrics to track it going forward.

About the team
Promise Insights team in Austin develops and manages various critical services for customer delivery promise to improve our ability to analyze, understand, forecast, and modify promise behavior with a predictable result. The team owns the Promise data pipeline which captures relevant data from Promise services; Promise Analytics Platform and Web tools like Promise Viewer provide meaningful insights to stakeholders (PMTs, RSs, BIEs, Economists, and SDEs) for data-driven decision-making. We also use that data to run simulations to help answer business-critical questions; e.g., what is the impact on delivery promises and Amazon business if we had infinite inventory in every warehouse? Simulation plays a key role in enabling teams to achieve their business goals by identifying and sizing opportunities for increased profitability through cost savings and customer experience improvements. Simulations also provide insight into promise speed variants and the factors that impact them.
The team is currently focused on evolving our capabilities into platforms that can be used across the company as well as expanding our Tools to not just present existing data, but to analyze that data against other systems to preemptively answer questions around what caused deviations in expected behavior.

- 3+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc. experience
- Experience with data visualization using Tableau, Quicksight, or similar tools
- Experience with data modeling, warehousing and building ETL pipelines
- Experience using SQL to pull data from a database or data warehouse and scripting experience (Python) to process data for modeling
- Experience developing and presenting recommendations of new metrics allowing better understanding of the performance of the business

- Master's degree in sciences, engineering, finance or equivalent
- Experience in data mining, ETL, etc. and using databases in a business environment with large-scale, complex datasets

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