Business Intelligence Engineer

Who you are

• 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
• Master's degree in BI, finance, engineering, statistics, computer science, mathematics or equivalent quantitative field
• Experience Productionize LLM-based diagnostic outputs into reliable datasets

What the job involves

• Amazon's Customer Service organization is seeking a Business Intelligence Engineer to join the Data Analytics Support Hub. Customer Service is the heart of Amazon
• Our vision is to be Earth's most customer-centric company, and this team plays a central role in understanding why customers need to contact us and how we can prevent those contacts from happening in the first place
• The Advanced Analytics team is evolving Quality and Escalations analytics from descriptive reporting ("what happened") to diagnostic ("why did it happen) analytics at a worldwide level
• We build the data infrastructure that powers multi-contact journey analysis, large-scale transcript processing, and machine-learning-driven root-cause identification across billions of customer interactions
• In this role, you will design and own the production data pipelines that turn domain expertise into scalable diagnostic insights
• You will work across the full data lifecycle, from ingestion and modeling through certification and serving, enabling stakeholders to self-serve answers to complex "why" questions without waiting for ad-hoc analysis
• Your work will directly feed Weekly Business Reviews, leadership dive-deeps, and the automation of diagnostic narratives using large language models
• This is an opportunity to combine deep data engineering craft with high-visibility business impact
• You will operate at the intersection of large-scale data infrastructure and applied AI, shipping systems that meaningfully improve how Amazon understands and acts on customer experience signals worldwide
• Build and own production data pipelines for diagnostic workloads: transcript ingestion at worldwide scale, multi-contact threading, journey-grain feature tables, and model-serving datasets
• Design and maintain end-to-end data models for the team's KPI portfolio, from raw source integration through consumption-ready tables
• Integrate team pipelines with central Customer Service data infrastructure, consuming shared tooling and contributing reusable components
• Scale innovations from analyst prototypes into maintainable, certified production pipelines with appropriate monitoring and alerting
• Build and maintain the transcript prototyping infrastructure used by stakeholders to self-serve, reducing time-to-delivery for new analytical requests
• Productionize LLM-based diagnostic outputs into reliable, refreshable datasets that power automated "why" narratives and self-service dive-deeps
• The Data Analytics Support Hub is a high-impact group responsible for diagnostic analytics across Amazon Customer Service worldwide. We answer the "why" behind quality and experience trends
• Our team combines business Analyst, data engineering, data science, and business intelligence expertise
• We operate across the full stack: from raw transcript ingestion pipelines through feature engineering, LLM-based classification, and executive-facing automated narratives
• We partner closely with operations, science, and product teams across CS to translate complex multi-interactions journey data into actionable insights that drive real improvements for customers