ADCI
Technology
BusinessIntelligenceEngineer-II,SalesAI
Neural analysis suggests this role is
optimal for Mid candidates.
“Business Intelligence Engineer- II, Sales AI at ADCI. Skills: Data engineering, Business intelligence, Analytics engineering, AI. Design and build data pipelines. Ingest, transform, and serve advertiser context”
What You'll Achieve.
Serve rich, accurate advertiser context; Understand agent performance and business impact; Track agent performance; Track context quality; Track business impact
Industry & Context.
Data analysis; Identify coverage gaps; Identify quality issues; Identify opportunities to enrich context
What They're Looking For.
Must Have
Data modeling experience, Warehousing experience, Building ETL pipelines experience, 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, Experience with large-scale data warehousing (Redshift, Spark, or equivalent)
Nice to Have
Experience in data mining, Experience in ETL, Experience 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
What You'll Do.
Design and build data pipelines
and serve advertiser context
Create and maintain data models
Unify fragmented advertiser information
Build measurement and reporting infrastructure
Track agent performance
Partner with software engineers to design data serving
Optimize for low-latency agent retrieval
Conduct deep data analysis
Identify coverage gaps
Develop dashboards and automated reporting
Define data quality standards
Collaborate with Applied Scientists on feature engineering
Prepare data for machine learning models
How You'll Work.
Team & Collaboration
Partner with software engineers; Collaborate with Applied Scientists
Full Job Description
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. - Buil
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