Senior Data Engineer (EU Books Analytics and Engineering)

Requirements

• Experience with data modeling, warehousing and building ETL pipelines
• Experience with SQL
• Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS
• Experience mentoring team members on best practices
• Experience as a Data Engineer or in a similar role
• (Desirable) Experience communicating with users, other technical teams, and management to collect requirements, describe data modeling decisions and data engineering strategy
• (Desirable) Experience providing technical leadership and mentoring other engineers for best practices on data engineering
• (Desirable) Knowledge of distributed systems as it pertains to data storage and computing
• (Desirable) Experience building/operating highly available, distributed systems of data extraction, ingestion, and processing of large data sets

What the job involves

• 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
• 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
• You start your day reviewing pipeline health across 50+ recurring jobs via the monitoring dashboard you helped build. Mid-morning, you partner with a BIE to design a new datamart schema for a business experiment launching across multiple marketplaces. After lunch, you debug a data quality issue in a cross-domain pipeline, then join a sprint sync where engineers share progress signals
• Late afternoon, you architect the data layer for an AI agent that will let analysts query demand drivers conversationally. Your work feeds dashboards leadership uses weekly and AI tools that are reshaping how the organization operates

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