Are you interested in shaping the future of Advertising and B2B Sales? Amazon Advertising is one of Amazon's fastest growing and most profitable businesses, delivering billions of ad impressions and generating billions in revenue.
We are looking for a Business Intelligence Engineer to join our team and build the data foundations that power an advertiser context center — the contextual backbone behind our AI agents. You will design data models, build pipelines, and create the analytical infrastructure that enables our agents to serve rich, accurate advertiser context to account teams at scale.This role sits at the intersection of data engineering, analytics, and AI. You will define how advertiser data flows from dozens of source systems into a unified context layer that agents can reason over — while also building the reporting and measurement systems that help us understand agent performance and business impact.
Why You Will Love This Opportunity
- Impact at scale: Your data pipelines and models directly power AI agents used by thousands of account managers serving Amazon's largest advertisers globally.
- Greenfield data architecture: The advertiser intelligence center is being built now — you'll define how data is modeled, integrated, and served from the ground up.
- AI-adjacent work: Build the data substrate that LLM-based agents consume — a new class of data engineering problem.
- End-to-end ownership: From raw source ingestion to agent-ready serving layers to business impact measurement.
- Entrepreneurial team:We move fast, experiment often, and ship real products. Small team, big mandate.
Key job responsibilities
- Design and build data pipelines that ingest, transform, and serve advertiser context from dozens of source systems (campaign data, deal history, behavioral signals, conversation transcripts, account metadata).
- Create and maintain data models that unify fragmented advertiser information into a coherent, queryable representation for AI agents.
- Build the measurement and reporting infrastructure that tracks agent performance, context quality, and business impact (adoption, accuracy, revenue influence).
- Partner with software engineers to design data serving layers optimized for low-latency agent retrieval.- Conduct deep data analysis to identify coverage gaps, quality issues, and opportunities to enrich advertiser context.
- Develop dashboards and automated reporting for weekly business reviews and leadership visibility.
- Define data quality standards and implement validation, monitoring, and alerting for production data pipelines.
- Collaborate with Applied Scientists on feature engineering and data preparation for machine learning models.
About the team Within the Advertising Sales organization, Sales AI is building the intelligent systems that transform how account teams operate — from actionable insights and recommendations to Generative AI-powered agents that streamline end-to-end sales workflows
Basic Qualifications
- Experience with data modeling, warehousing and building ETL pipelines
- Bachelor's degree or above in engineering, statistics, computer science, mathematics, or a related quantitative field
- 3+ years of experience in business intelligence, data engineering, or analytics engineering
- Expert SQL skills and experience with large-scale data warehousing (Redshift, Spark, or equivalent)
Preferred Qualifications
- Experience in data mining, ETL, etc. and using databases in a business environment with large-scale, complex datasets
- Experience scripting for automation (e.g., Python, Perl, Ruby)
- Experience building large-scale machine learning models and infrastructure for online recommendation, ads ranking, personalization, or search
- Experience with AWS services including S3, Redshift, Sagemaker, EMR, Kinesis, Lambda, and EC2
- Familiarity with real-time or near-real-time data serving patterns (streaming, change data capture, materialized views). Exposure to Generative AI data requirements — embeddings, vector stores, or context preparation for LLMs-
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