Operational Excellence Analyst , Global Operational Excellence at Amazon.com

Amazon.com; Madrid; Presencial; EUR 85.000 - 105.000; Jornada completa

Descripción de la vacante

Amazon.com in Madrid is seeking an Operational Excellence Analyst to drive data-driven performance improvements across EU inbound supply chain. You will own analytics, build scalable data pipelines, and develop tools to identify defects and improve customer delivery outcomes.

You will write production-grade SQL, develop Python automation, design dashboards, and collaborate with cross-functional teams to translate findings into system changes that prevent issues and accelerate insight delivery.

Formación

• Bachelor's degree or equivalent in math, engineering, science or business.
• 3+ years of experience in data analytics, BI, or data engineering within operations/supply chain.

Responsabilidades

• Own the data infrastructure behind Inbound Speed and DEA metric reporting with ETL pipelines, scheduled queries, and alerting mechanisms.
• Write advanced SQL against large-scale data warehouses to perform root cause analysis on placement and transportation defects.
• Develop Python-based automation for data processing, anomaly detection, and reporting workflows.
• Build statistical models to quantify the impact of transportation disruptions on customer delivery promises.
• Design and maintain dashboards and self-service analytical tools for stakeholders to detect problems before customer impact.

Conocimientos

Data analytics

Data engineering

BI tooling

Educación

Bachelor's degree or equivalent in Math, Engineering, Science or Business

Herramientas

SQL

Redshift

Athena

DynamoDB

Python

Pandas

Numpy

Boto3

Descripción del empleo

DESCRIPTION The Operational Excellence Analyst in Global Operational Excellence (GOX) drives data-driven performance improvements across Amazon’s EU Inbound Supply Chain operating in a cross-functional environment with Cross Dock and Fulfillment Center Operations, Transportation, Analytics, and Planning Systems teams.

This role owns the technical analysis and automation behind regional Inbound Speed and Delivery Estimate Accuracy (DEA) metrics. You will build scalable data pipelines, write production-grade SQL against petabyte-scale warehouses, and develop analytical tooling to identify, quantify, and eliminate placement defects, transportation disruptions, and supply chain backlog connecting systemic root causes to shop-floor impact and customer delivery outcomes. You will develop automated monitoring systems, statistical models, and simulation frameworks to surface improvement opportunities before they impact customers. Your work will span from exploratory data analysis on large-scale datasets to building self-service tools that enable business partners to make data-driven decisions in real time.

Key job responsibilities

• Own the data infrastructure behind Inbound Speed and DEA metric reporting building automated ETL pipelines, scheduled queries, and alerting mechanisms
• Write advanced SQL (window functions, CTEs, recursive queries, query optimisation) against large-scale data warehouses (Andes/Redshift, Athena) to perform root cause analysis on placement and transportation defects
• Develop Python-based automation for data processing, anomaly detection, and reporting workflows reducing manual intervention and accelerating insight delivery
• Build statistical models to quantify the impact of transportation disruptions, supply chain backlog, and placement deficiencies on customer delivery promises
• Design and maintain dashboards and self-service analytical tools (QuickSight, automated reports) enabling stakeholders to detect problems ahead of customer impact
• Drive the process improvement roadmap with data-backed proposals influencing decision-making at all levels through rigorous quantitative analysis
• Perform large-scale data mining across multiple database systems (Redshift, Athena, DynamoDB) to identify patterns in transship flows, delivery accuracy, and network performance
• Collaborate with technology teams to translate analytical findings into system requirements, new features, and configuration changes
• Develop and document automated data quality checks and monitoring frameworks for critical operational metrics
• Act as technical enabler for front-line teams building tools that surface defects, identify root causes, and track corrective action effectiveness

A day in the life

• Write and optimise complex SQL queries across multiple data sources to investigate why customer delivery promises were missed ‑tracing issues back to specific products, warehouses, or shipping routes
• Build and maintain Python scripts that automate daily/weekly data pulls, transformations, and reporting deliverables
• Collaborate with Business Intelligence Engineers, Program Managers, and Finance Analysts to develop scalable metrics frameworks
• Perform deep‑dive investigations on specific shipments or orders, then scale findings using statistical methods to quantify network‑wide impact
• Translate operational challenges raised by business teams into structured analytical problems ‑ designing technical solutions that address the root cause, not just the symptom
• Partner with tech teams to define data requirements and validate system changes through pre/post analysis
• Present data‑driven recommendations to senior leadership with clear quantification of customer and cost impact
• Develop and maintain scheduled data transformation jobs for automated reporting
• Monitor automated alerting systems and triage emerging operational issues using real‑time data

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 or equivalent qualification in Math, Engineering, Science or Business
• 3+ years of experience in data analytics, business intelligence, or data engineering within operations/supply chain

PREFERRED QUALIFICATIONS

• Python programming experience writing scripts for data manipulation (pandas, numpy), automation (boto3, scheduling), and basic statistical analysis

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