Automated Inventory Management (AIM) team is seeking for a Business Intelligence Engineer to lead Supply Chain projects for LATAM (Brazil and Mexico) being based in Brazil. This strategic role will be analyzing large sets of data to build insights and help making data-driven decisions. The Supply Chain BIE will work with Operations, business teams and other stakeholders to create metrics, track goals and implement projects. The candidate should be comfortable working within a cross-functional environment, supporting multiple stakeholders and initiatives. The candidate must have a true hands-on approach, strong analytical skills, including the ability to gain true insights from data, and be an effective communicator.
Key job responsibilities
- Work with teams in the organization to identify opportunities and build analytical solutions that provide them with decision making capabilities and insights.
- Partner with Supply Chain and Operations teams proactively identify analytical needs, translate them to BIE/Science solution designs.
- Identify, develop, manage, and execute analyses to uncover areas of opportunity and present written business recommendations that will help shape the direction of analytics projects/ products.
- Own the design, development, and maintenance of ongoing metrics, reports, analyses, dashboards, etc. to drive key business decisions. Ensure data accuracy by validating data for new and existing tools.
- Design and influence operational best practices for reporting and predictive analytics to enable the team to scale.
- Root cause analysis and uncovering core issues using data, then assisting to create new measures for the wider Amazon team
- Manage and execute entire projects from start to finish including project management, data gathering and manipulation, synthesis and modeling, problem solving, and communication of insights and recommendations.
- Collaborate with Product and Program management teams to design analytics frameworks that will support large-scale data-analysis and on-ground corrective actions.- Experience in analyzing and interpreting data with Redshift, Oracle, NoSQL etc.
- 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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