Amazon Business Intelligence Engineer SPIA 

Mexico, Veracruz, Orizaba 

103340835

As a Business Intelligence Engineer on the SPIA Analytics Team, you will play a crucial role in managing and analyzing employee data while driving AI-powered automation initiatives. Your primary responsibilities include maintaining and optimizing the employee database, partnering with the Workforce Management (WFM) team to deliver accurate headcount metrics and attrition analyses, and spearheading the development of AI-driven solutions to automate manual processes and pipelines. In this role, you will be instrumental in leveraging artificial intelligence to enhance operational efficiency across the team. You'll collaborate closely with cross-functional teams to identify opportunities for AI implementation, design and develop innovative AI models, and integrate these solutions into existing workflows. This position combines data engineering, advanced analytics, and vanguard AI technologies to transform and optimize key organizational processes within the SPS organization."Key job responsibilities
1. Develop and maintain robust data frameworks and measurement mechanisms, focusing on employee data management and optimization of the employee database.3. Spearhead the development and implementation of AI-driven solutions to automate manual processes and pipelines, enhancing operational efficiency across the organization
5. Utilize advanced SQL and Python skills to conduct complex business decision-support analyses, including driver analysis and time series forecasting.7. Create and maintain effective visualizations and dashboards that tell compelling stories, providing actionable insights and recommendations for new business initiatives.
8. Continuously monitor data quality, optimize data flows, and provide training and support to business users, ensuring effective adoption and utilization of BI solutions across the organization.

- Experience with data visualization using Tableau, Quicksight, or similar tools
- Experience with Experimentation and Non Experimental Causal attribution models
- 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