As a Business Intelligence Engineer specializing in Cross-Border Business, you will drive data-led decision-making for Amazon’s global expansion initiatives. You will partner with cross-functional teams to define key performance indicators (KPIs), build scalable data pipelines, and deliver actionable insights that optimize cross-border operations, pricing, logistics, and market entry strategies. This role requires deep technical expertise in data engineering, analytics, and visualization, coupled with a nuanced understanding of international market dynamics.
Key Responsibilities
Define and Track Cross-Border KPIs
Collaborate with product, finance, and operations teams to identify and prioritize KPIs for cross-border business performance. Design dashboards and reports to monitor metrics such as sales growth, fulfillment efficiency, and regional profitability.
Build Scalable Data Pipelines
Develop and maintain ETL processes to ingest, transform, and analyze cross-border transactional data from diverse sources. Optimize data workflows for Amazon Redshift, Athena, or similar platforms to support real-time analytics.
SQL and Advanced Analytics
Write complex SQL queries to validate data integrity, automate reporting, and support tests for cross-border initiatives. Apply statistical methods to analyze regional market trends and customer behavior.
Cross-Functional Partnership
Translate ambiguous business questions from global stakeholders into measurable data solutions. Lead investigations into operational anomalies and recommend process improvements.
Data Visualization and Storytelling
Create interactive dashboards to communicate insights to non-technical audiences. Highlight conversion funnel drop-off points and propose data-driven strategies for improvement.
- 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 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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