Sr. Analyst , Global Operational Excellence

Amazon is hiring a Sr. Analyst , Global Operational Excellence in Madrid, Spain. Level rates it ; you can apply on Amazon.

Level's read on this job

On Level's scale, this role is Works on AI ●●●○: the daily work is on AI products, without building the model.
The posting mentions SageMaker, prompt engineering and RAG among its tools and skills.
Amazon has 20,000+ other open roles on Level.

AI in this role

SageMaker

prompt-engineeringragllm-integrationai-agents

Please note: This job is advertised for Luxembourg but can be located in any of our EU sites or offices.

We are seeking a Senior Analyst to own the analytical backbone across multiple high-impact domains within EU Inbound Supply Chain. This is not a single-metric reporting role you will design scalable data architectures, build production-grade analytics systems, and drive strategic decisions through data, working across both real-time streaming and batch processing to get the team the right data at the right time.

The ideal candidate pairs deep technical expertise in data engineering and analytics with the ability to influence cross-functional stakeholders and turn ambiguous business problems into measurable solutions. You're equally comfortable building scalable pipelines on AWS services over terabytes of data, architecting a real-time monitoring solution, presenting to senior leadership, or prototyping GenAI tools to scale analytical capacity.

What makes this role unique:
- Context-switching is the job - one week you're decomposing a delivery-accuracy variance across the EU network; the next you're building a real-time Inbound Management pipeline; the week after you're prototyping a GenAI tool that auto-generates root-cause narratives for business reviews.
- Federated stakeholder model - you'll work across Engineering, Science, Transportation, Planning, Finance, and Operations. Aligning on definitions and building shared analytical frameworks matters as much as writing the code.
- GenAI as a force multiplier - you won't just use AI tools, you'll build them: agentic deep-dives, automated narrative generation, and intelligent alerting that scale analysis across inbound speed, DEA, and transportation optimization.

Key job responsibilities
- Design and own end-to-end data architectures spanning real-time streaming (Kinesis, Flink) and batch processing (EMR, Redshift, Athena), selecting the right paradigm for the latency, volume, and criticality at hand.
- Build production-grade, scalable ETL/ELT pipelines processing multi-TB datasets daily across Redshift, Athena, S3, and DynamoDB with data-quality checks, monitoring, and alerting.
- Architect real-time analytics for operational monitoring: live visibility into inbound flows, delivery accuracy, dock congestion, and network disruptions.
- Own metric frameworks and reporting: design KPIs, build automated dashboards (QuickSight), support WBR/MBR narratives, and own the “why” through root-cause analysis when metrics move.
- Develop GenAI-driven tooling and agentic workflows that automate reporting narratives and root-cause analysis, reducing time-to-insight from days to minutes.
- Drive deep-dive investigations across complex, multi-source datasets tracing issues to specific products, warehouses, or routes, then quantifying network-wide impact with statistical methods.

A day in the life
You'll have exposure to the full data-product lifecycle — from defining metrics to designing architectures, building pipelines, and delivering insights to senior leadership. A typical week might include:
- Designing pipelines that unify real-time event streams (shipment scans, trailer movements, inventory updates) and batch sources (forecasts, historical performance) into a single analytical layer.
- Writing advanced SQL across multiple sources to investigate why delivery promises were missed from the customer order through placement, transportation, and fulfillment.
- Building Python-based automation for anomaly detection, statistical modeling, and reporting.
- Iterating on GenAI-powered tools and presenting data-driven recommendations, with clear quantification of customer and cost impact, to senior leadership.
- Triaging emerging operational issues from real-time dashboards and providing data-backed context.

About the team
The Global Operational Excellence (GOX) team drives operational improvements across Amazon’s EU fulfillment network and ROW. We work at the intersection of technology and operations to optimize how inventory is placed, moved, and delivered reducing costs while improving customer experience. Our team leverages large-scale data analysis, automation, and cross-functional collaboration to solve complex supply chain challenges across Inbound Speed, Delivery Estimate Accuracy (DEA), and network performance.

Basic qualifications

- Bachelor's degree in a quantitative field such as statistics, mathematics, data science, computer science, economics, engineering, or operations research.
- Experience in analytics, data engineering, or business intelligence at a large-scale enterprise, including data-warehouse architecture, data modeling, and ETL/ELT pipeline design.
- Advanced SQL and programming (Python or equivalent) to extract, transform, and clean large (multi-TB) datasets, plus scripting for pipeline automation and scheduling.
- Experience with both real-time streaming (e.g., Kinesis, Kafka, Flink) and batch processing (e.g., EMR, Hive) systems, and with data-visualization tools (QuickSight, Tableau, or similar).
- Experience translating operational problems into analytical solutions and communicating results to senior leadership (Director/VP) with clear business-impact quantification.

Preferred qualifications

- Master's degree in statistics, data science, computer science, operations research, or an equivalent quantitative field.
- Experience with AWS services including S3, Redshift, Athena, EMR, Kinesis, Lambda, Glue, Step Functions, SageMaker, and DynamoDB.
- Experience with GenAI/LLM integration - building AI-assisted analytical tools, prompt engineering, RAG architectures, or agentic workflows.
- Experience with statistical analysis and experimentation (design of experiments, causal inference, A/B testing) and ML/data-mining pipelines.
- Knowledge of supply-chain operations (inventory placement, network optimization, transportation, fulfillment) and Amazon internal data systems (Andes/Redshift, Cradle, Data Central, EDX).

Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build. Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult our Privacy Notice (https://www.amazon.jobs/en/privacy_page) to know more about how we collect, use and transfer the personal data of our candidates.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

How we rate this

Sr. Analyst , Global Operational Excellence at Amazon rates 64 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.

Classification

Works on AI. The daily work is on AI products, without building the model.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Sr. Analyst , Global Operational Excellence is an open job at Amazon.
Level rates this role AI Level 3 of 4: Works on AI.
The role is based in ES, M, Madrid.
The job was posted on September 5, 2026.
Applications are handled by Amazon. Apply on Amazon

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Prompt engineeringRAGLLM IntegrationAI agentsSageMaker

Questions you could be asked

How do you structure and test a prompt to get consistent output from a language model?
How would you design a retrieval step so the model answers from real data instead of guessing?
How have you integrated a large language model into a production application?
How do you decide when an AI agent can act on its own versus asking for approval first?
Walk me through how you've used SageMaker in your day-to-day work.

Adapt your resume

List these exact terms on your resume: Prompt engineering, RAG, LLM Integration, AI agents, and SageMaker. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.

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