We are looking for a customer obsessed Business Intelligence Engineer who is comfortable with ambiguity and excited to build intelligence systems that help Account Managers and leaders make better, faster decisions at scale. You should be comfortable working across the full stack of BI and applied ML: writing production SQL, building data pipelines, developing and evaluating ML models (classification, scoring, ensemble methods), designing MCP tool definitions and knowledge schemas, and delivering actionable insights through dashboards and AI powered interfaces. You will work closely with product, science, and engineering teams

Key job responsibilities

Design and build MCP server as a centralized knowledge repository, exposing lead context, seller signals, program rules, and historical outcomes to AI agents and tools across Amazon MCP compliant platforms
Develop and maintain ML driven scoring and prioritization models (intent scoring, conversion prediction, multi signal ensemble models) working alongside the shared Data Scientist, with ownership of feature engineering, model evaluation, and production deployment
Create, support and continuously improve reports and metrics that support the business operations
Design and implement data pipelines (SQL, Python, Airflow) that process seller signals, engagement data, and pipeline metrics into actionable intelligence
Drive automation of repetitive reporting and assignment tasks to scale intelligence delivery across regions
Convert data into digestible business intelligence and actionable information using tools like QuickSight/Tableau
Shape and deliver key metrics, reports, and indicators by which our business will assess its performance
Dive deep into large data sets to answer specific business questions using Excel, SQL and other data manipulation languages
Work directly with business teams to utilize metrics and analysis to determine improvement opportunities
Identify process improvement opportunities and drive automation to scale reporting solutions
Mentor junior analysts and provide guidance on best practices for data analysis and reporting
Independently manage end-to-end project delivery with minimal supervision