Business Intelligence Engineer, Supply Chain Innovation, Bulk Fulfillment
Listed on 2026-07-22
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IT/Tech
Data Analyst, Data Engineering, Data Warehousing
Description
Imagine revolutionizing how millions of customers shop for bulk products by creating effective data solutions that drive strategic decisions. As a Business Intelligence Engineer, you'll be the critical analytical force behind Amazon's ambitious expansion into bulk fulfillment, translating complex data into actionable insights that shape the future of retail.
This role offers comprehensive end-to-end exposure across Amazon's supply chain, from vendor inbound through customer delivery, with direct impact on a strategic initiative that will reshape customer perception of Amazon as a destination for bulk purchases.
Key job responsibilitiesData Infrastructure & Analytics
Design and build scalable data pipelines synthesizing information from multiple sources:
Asana (task management), Slack (real-time updates), WBRs (weekly operations), MBRs (monthly business reviews), Quick Sight Topics (live metrics), and operational systems (SCOT, AFT, MSP)
Create automated reporting mechanisms for Senior leadership providing weekly program health snapshots across all 12-13 work streams with clear status, risks, dependencies, and next steps
Develop data quality frameworks ensuring accuracy and consistency across expanding network of 24 One DCs and 13 SDCs
Create executive dashboards and one-pagers for Senior leadership with appropriate context for first-time readers, including trade-offs, risks, and stakeholder implications
Develop site launch readiness scorecards combining operational metrics, technical readiness, capacity data, and risk assessments to inform go/no-go decisions
Build dependency mapping visualizations showing relationships between work streams, identifying critical paths and potential bottlenecks
Build predictive models for capacity planning across distribution centers, forecasting volume, labor requirements, and equipment needs for site launch readiness assessments
Develop selection optimization models identifying which bulk ASINs to onboard based on customer purchase patterns, inventory availability, SIOC eligibility, and profitability metrics
Create demand forecasting models for bulk conversion rates (single-unit to bulk purchases) leveraging internal signals and competitive intelligence
Own end-to-end analytics for three critical pillars:
Quality (DEA - Delivery Estimate Accuracy), Speed (click-to-promise), and Cost (productivity rates: pick rate, pack rate, cartons per labor hour)
Conduct deep-dive root cause analysis when metrics degrade, synthesizing quantitative data (miss units by site/day/attribution) with qualitative context (W narratives, Slack discussions, operational feedback)
Build comparative analytics showing bulk performance versus regular component ASIN fulfillment to quantify program impact
Partner with Product Management, Supply Chain, Operations, and Technology teams across 10+ VP organizations to define metrics, validate data accuracy, and translate business questions into analytical frameworks
Work with finance teams on business case development, ROI modeling, and cost-benefit analysis for capital planning and site enablement investments
Collaborate with international teams (Canada, EU) to establish consistent metrics definitions and reporting standards for geographic expansion
3+ years of analyzing and interpreting data with Redshift, Oracle, No
SQL etc. experience1+ years of SQL, ETL or Oracle experience
1+ years of processing large, multi-dimensional datasets from multiple sources experience
1+ years of performing statistical analysis experience
1+ years of developing automated reporting experience
Experience with data visualization using Tableau, Quicksight, or similar tools
Experience with data modeling, warehousing and building ETL pipelines
Experience in Statistical Analysis packages such as R, SAS and Matlab
Experience using SQL to pull data from a database or data warehouse and scripting experience (Python) to process data for modeling
Experience with AWS solutions such as EC2, DynamoDB, S3, and Redshift
Experience in data mining, ETL, etc. and using databases in a business environment with large-scale, complex datasets
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