Amazon.com Services LLC

Business Intelligence, Business Intel Engineer, alexa and amazon devices

AnalyticsEngineer(AI),ReverseLogistics(RL)

$115–185k Sunnyvale, California, United States FULL TIME
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Mid+ candidates.

The Brief

“Analytics Engineer (AI), Reverse Logistics (RL) at Amazon.com Services LLC. Skills: Generative AI, Machine learning, Analytics engineering, Multi-agent systems. Build AI-driven analytics platform. Develop intelligent systems”

Industry & Context.

Business Intelligence, Business Intel Engineer, alexa and amazon devices
Problems you'll solve

Data-driven decision making; Troubleshooting

What They're Looking For.

Must Have

5+ years analyzing data, 3+ years processing large datasets, Experience using SQL, Scripting experience (Python), Experience in Statistical Analysis packages, Experience with data visualization, Proficiency with AI tools, Building multi-agent systems, Integrating generative AI, Applying reinforcement learning, Applying optimization algorithms, Experience with AWS DevOps/AI/ML services, Deploying RAG applications

Nice to Have

Experience with AWS solutions, Experience in data mining, Experience building ETL pipelines, Experience with Data & AI technologies

What You'll Do.

Build AI-driven analytics platform

Develop intelligent systems

Enable natural language querying

Enable automated insight generation

Enable workflow orchestration

Design agentic solutions

Build intelligent automations

Deliver predictive recommendations

Deliver actionable insights

Deliver proactive alerts

Curate foundational data layers

Implement governance frameworks

Enable AI tools to leverage data

Enhance analytics processes

Explore AI/ML integration

Translate business requirements

Create multi-agent frameworks

Enable tailored querying

Enable analytics access

Establish guidelines for responsible AI use

Implement best practices for model monitoring

Implement A/B testing

Implement continuous improvement

Build and maintain ETL pipelines

Build semantic data models

Experiment with AI/ML tools

Experiment with agentic frameworks

Evaluate capabilities

How You'll Work.

Team & Collaboration

Partner with Data Engineering; Partner with Applied Science; Partner with Strategy; Partner with Product; Stakeholder collaboration

Communication Scope

Communicate findings; Communicate value; Communicate limitations

Full Job Description

The Amazon Devices Reverse Logistics (ADRL) team seeks an Analytics Engineer specializing in AI-augmented analytics – You'll pioneer intelligent agentic systems by building multi-agent frameworks that enable natural language querying, automated insight generation, and intelligent workflow orchestration. You'll establish a curated, certified foundational data layer with robust governance, making Reverse Logistics data seamlessly accessible to AI tools and customers. The ideal candidate combines expertise in generative AI, machine learning, and modern BI engineering—architecting solutions that unlock advanced analytical capabilities while maintaining enterprise-grade quality, security, and scalability. Key job responsibilities • Build and evolve AI-driven analytics platform for ADRL organization – Develop intelligent systems using multi-agent frameworks that enable natural language querying, automated insight generation, and workflow orchestration to accelerate delivery, reduce manual effort, and scale BI solutions • Design and deploy agentic solutions for Reverse Logistics Business – Leverage Python and AWS services (SageMaker, Bedrock, Lambda) to build intelligent automations for business workflows with real-time insights, delivering predictive recommendations, actionable insights, and proactive alerts to executive leadership • Curate foundational data layers and implement governance frameworks – Enable AI tools to leverage high-quality, semantically modeled data for business decision-making across ADRL • Partner with Data Engineering and Applied Science teams – Enhance data sources and analytics processes, explore AI/ML integration opportunities for more scalable and accurate reporting, and translate business requirements into scalable automated solutions • Create multi-agent frameworks serving as self-service hubs – Enable tailored querying and analytics access for all RL stakeholders across organizational data • Document patterns and establish guidelines for resp

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