Operational Excellence Analyst , Global Operational Excellence

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. The role requires deep technical proficiency in data extraction, transformation, and analysis combined with the ability to translate complex findings into actionable recommendations for senior leadership. You will design and implement end-to-end analytical solutions: from raw data ingestion through insight delivery, working across Andes/Redshift, Athena, S3, Python automation, and visualization platforms. We are seeking a technically strong specialist with passion for data engineering, process improvement, and operational analytics someone who can independently mine complex datasets, identify systemic patterns, and drive measurable improvements to customer delivery experience across EU marketplace. The applicable collective agreement for this role is CBA for employees of Logistics, Delivery and Shipping Sector. The position is classified at level 1 or above, depending on the candidate's skills, competences and experience. 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 optimization) 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 optimize 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 -