[Hiring] Senior Analyst, Global Operational Excellence @Amazon

Role Description

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.

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).

Company Description

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.

Our inclusive culture empowers Amazonians to deliver the best results for our customers.