Business Intelligence Engineer, ISFX
Confirmed on the employer's own hiring board on Sep 28, 2026. First seen by Alion on Sep 11, 2026.
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
Basic qualifications
- 3+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc. experience
- 2+ years of Tableau Desktop, Quicksight or other relevant data visualization software experience
- Bachelor's degree or above in business administration, finance, economics, computer science, data science, engineering, or other related field, or 2+ years of Amazon RME (BB/3P) Full Time Exempt 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 using SQL (Structured Query Language) to pull data from a database or data warehouse
- Experience using Python scripting to process data for modeling
- Proficiency in Python for data manipulation, automation, and ML model development
Preferred qualifications
- Master's degree or above in BI, finance, engineering, statistics, computer science, mathematics or equivalent quantitative field
- Knowledge of Microsoft Excel at an advanced level, including: pivot tables, macros, index/match, vlookup, VBA, data links, etc.
- 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 developing and presenting recommendations of new metrics allowing better understanding of the performance of the business
- Experience building or integrating with MCP servers, API gateways, or similar knowledge serving architectures
- Experience with ML model deployment in production: scoring pipelines, A/B testing frameworks, model monitoring and retraining
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