As a Business Intelligence Engineer with Worldwide Customer Services Finance team, you will help identify opportunities and implement solutions to improve Finance facing systems and tools. You will be responsible for functional and technical design of BI solutions for the team, including user interfaces, data architecture and data processing. Further, you will drive automation of processes through self-service deep-dive tools, and dashboards to deliver insights to the business teams. While onboarding new functionalities you’ll ensure the solution implemented will fulfill business needs in an efficient scalable way.
Everything we do at Amazon is focused on being “Earth’s Most Customer-Centric Company”. At the core of this customer centricity is the Amazon Customer Service organization. We are currently looking for a highly analytical, creative and forward-thinking Business Intelligence Engineer to join our Customer Service Finance team.
Key job responsibilities
You will partner with Engineering, Operations Finance and Capacity Planning teams around the world to standardize our processes, improve front-end reporting, and create mechanisms to scale tools to accommodate rapidly changing business needs. You will be responsible for building and maintaining efficient near real-time data pipelines that bring together important operations and finance data from disparate data sources.
3+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc. experience
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2+ 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 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
Experience in Statistical Analysis packages such as R, SAS and Matlab
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