Business Analyst, RBS Emerging Stores

About Retail Business Services (RBS)
RBS strengthens Amazon’s retail engine by driving standardization, improving process quality, and reducing defects across global marketplaces. RBS operates at the intersection of retail, operations, technology, and finance, ensuring Amazon’s systems and processes scale reliably while maintaining a high-quality customer and selling-partner experience.
About Cost to Serve (CtS)
CtS is a specialized organization within RBS focused on improving Amazon's per-unit economics across the global supply chain. CtS operates on two pillars: defect identification and defect elimination, and partners with WW Operations, Finance, Returns, Concessions, and abuse-related programs to uncover true cost drivers and remove them systematically. The team builds mechanisms that improve cost predictability, strengthen upstream defect visibility, and enable consistent governance across high-impact cost categories, contributing directly to improved Free Cash Flow and scalable business growth.
Key Job Responsibilities
Design, develop, and own the end-to-end datamart onboarding pipeline for Emerging marketplaces—defining data ingestion frameworks, schema design, validation logic, and quality standards that ensure comprehensive coverage across CtS cost categories (Damages, Shipping COGS, Returns, Operations).
Build and maintain automated ETL processes and data models that ingest, transform, and serve marketplace-specific data, resolving coverage gaps (e.g., missing country-level attributes, carrier data, concession reason codes) to enable accurate defect identification.
Develop scalable defect identification mechanisms using statistical analysis, anomaly detection, and pattern recognition to proactively surface cost drivers, data quality issues, and marketplace anomalies before they impact per-unit economics.
Write complex SQL queries and build analytical models to support root cause analysis, ad-hoc deep dives, and dive-deep investigations for operational or customer/vendor escalations across Emerging markets.
Build and maintain scalable dashboards and reporting systems in QuickSight/Tableau that surface defect trends, onboarding progress, cost driver decomposition, and data quality metrics for business reviews (WBR/MBR/QBR).
Own data infrastructure end-to-end—partner with Data Engineering and Tech teams to establish and maintain required tables, data lakes, and data structures that enable timely insights for CtS and IES country stakeholders.
Enable data-driven decision making by converting large datasets into digestible narratives, financial/CX estimates, and cost impact quantification for pilots, experiments, and market-specific expansions.
Partner with cross-functional teams (WW Operations, Finance, Returns, Concessions) to understand country-level requirements, define KPIs, automate reporting processes, and drive data-aligned solutions that meet evolving Emerging marketplace needs.
Prepare and deliver data-backed business reviews and written narratives for senior leadership, highlighting onboarding progress, defect opportunity sizing, risks, and decision points.
Participate in strategic planning discussions, providing analytical input that shapes long-term Emerging country data architecture and CtS defect elimination strategy across global regions.
- 3+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc. experience
- 2+ years of Tableau Desktop, Quicksight or other relevant data visualization software experience
- Bachelor's degree or above in business administration, finance, economics, computer science, data science, engineering, or other related field, or 2+ years of Amazon RME (BB/3P) Full Time Exempt 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 using SQL (Structured Query Language) to pull data from a database or data warehouse
- Experience using Python scripting to process data for modeling
- Master's degree or above in BI, finance, engineering, statistics, computer science, mathematics or equivalent quantitative field
- Knowledge of Microsoft Excel at an advanced level, including: pivot tables, macros, index/match, vlookup, VBA, data links, etc.
- 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
- Experience developing and presenting recommendations of new metrics allowing better understanding of the performance of the business
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