Business Intelligence Engineer

Description

We are seeking a customer-obsessed Business Intelligence Engineer who thrives in a data-driven decision-making culture to uphold a high standard for RBS BQA. This role will be responsible for driving and creating innovative solutions.

Key Job Responsibilities

• Leverage Generative AI tools and Large Language Models (LLMs) to automate data analysis, generate insights, and create natural language summaries of complex datasets.
• Build and deploy GenAI-powered analytics solutions to enhance self-service capabilities and accelerate decision-making.
• Develop prompt engineering strategies and RAG (Retrieval-Augmented Generation) pipelines to integrate GenAI with existing data infrastructure.
• Experiment with foundation models (e.g., Amazon Bedrock, SageMaker JumpStart) to solve business intelligence challenges.
• Work with large, multi-dimensional datasets from multiple sources.
• Make recommendations for new metrics, techniques, and strategies to improve operational and quality metrics.
• Proficiently use at least one data visualization product (Tableau, Qlik, Amazon QuickSight, Power BI, etc.).
• Experience in deploying Machine Learning and Statistical models.
• Build new Python utilities and maintain existing ones.
• Enable more efficient ad hoc queries & analysis.
• Collaborate closely with research scientists, business analysts, and product leads to scale data.
• Ensure consistency between various platform, operational, and analytic data sources to enable faster and more efficient detection and resolution of issues.
• Explore and learn the latest AWS technologies to provide new capabilities and increase efficiencies.
• Mentor the team on analytics best practices.

A Day in the Life

• Collaborate closely with cross-functional teams including Product/Program Managers, Software Development Managers, Applied/Research/Data Scientists, and Software Developers.
• Build dashboards, perform root cause analysis, and share actionable insights with stakeholders to enable data-informed decision making.
• Lead reporting and analytics initiatives to drive data-informed decision making.
• Design, develop, and maintain ETL processes and data visualization dashboards using Amazon QuickSight.
• Transform complex business requirements into actionable analytics solutions.

About the Team

Retail Business Service (RBS) accelerates worldwide Amazon Stores growth by improving customer and Selling Partner experiences while optimizing costs. Our three-fold charter includes: 1) Detecting and fixing customer shopping impediments, 2) Growing Selling Partner business profitably, including scaled tier-2/3 management, and 3) Reducing Cost-to-Serve across the Stores P&L. RBS Business Quality Assurance (BQA) transforms organizational capability through four pillars: Quality Assurance, People Excellence, Continuous Improvement, and Innovation. The team proactively solves internal challenges via systematic defect identification and data-driven frameworks. Through various programs, BQA positions RBS as Amazon's benchmark organization.

Basic Qualifications

• 5+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL, etc. 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 developing and presenting recommendations of new metrics allowing better understanding of the business performance.
• Experience writing complex SQL queries.
• Bachelor's degree in BI, finance, engineering, statistics, computer science, mathematics, finance, or an equivalent quantitative field.

Preferred Qualifications

• 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.
• Master's degree in BI, finance, engineering, statistics, computer science, mathematics, finance, or an equivalent quantitative field.