Amazon EU Books serves millions of customers across European markets with one of the world's largest book selections, spanning Kindle, print, and audiobook formats. The EU Books BI and Data Engineering team is transforming from a traditional reporting function into an AI-enabled decision intelligence engine. We are building the data foundation that powers self-service analytics, predictive models, and domain-specific AI applications across the EU Books organization. We are looking for a Data Engineer III to own and evolve the data architecture that supports multiple business domains including Demand, Pricing, Deals, Finance, and EU Books Leadership. You will build the infrastructure layer that connects raw business signals into reliable, governed, model-ready datasets, enabling both operational reporting and the advanced analytics capabilities we are building toward. The current data landscape spans multiple systems, teams, and marketplaces. You will consolidate, govern, and automate it, reducing stakeholder dependence on manual BI work and enabling self-service access at scale. Key job responsibilities
Own and evolve team-level data architecture: ingestion, transformation, storage, serving, and monitoring across multiple EU marketplaces and business domains
Design and build scalable, self-healing data pipelines that integrate business signals from diverse sources (demand, pricing, customer behavior, operational metrics)
Define data models and schemas optimized for both operational reporting and statistical/econometric model consumption
Build automated data quality frameworks that ensure accuracy and reliability for high-stakes business decisions
Engineer self-service data access through metadata-rich catalogs, governed query layers, and dashboard-ready datasets that enable stakeholders to answer recurring questions without BI mediation
Build the measurement infrastructure for business experiments (A/B tests, weblabs), ensuring clean experiment data and statistically valid result datasets
Drive cost optimization and data governance across the analytics data estate: lineage tracking, metric definitions, access controls, and SLA definitions
Partner with BIEs, business stakeholders, and cross-functional teams to translate analytical requirements into robust, scalable data solutions
Contribute to the team's AI Engineering roadmap by building the data backbone that domain-specific AI applications consume (automated narratives, anomaly detection, natural language data access)
Break complex cross-domain problems into parallel workstreams and coordinate delivery across contributors