Sr. Data Engineer, EU Books Analytics and Engineering

Amazon EU Books serves millions of customers across European markets with one of the worlds 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 teams 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

A day in the life
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.

About the team
We are a distributed team of data and business intelligence engineers across Europe transforming EU Books analytics from traditional reporting into an AI-enabled decision intelligence engine. Operating across four pillars Data Engineering Business Intelligence Advanced Analytics and AI Engineering we support Demand Pricing Deals Finance and Kindle Unlimited across 15 European marketplaces. We build self-healing pipelines AI-powered tools and self-service platforms that let business teams act faster. Our culture values ownership craftsmanship and depth over breadth L6 engineers own workstreams end-to-end with full autonomy.

- 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

- Experience communicating with users other technical teams and management to collect requirements describe data modeling decisions and data engineering strategy
- Experience providing technical leadership and mentoring other engineers for best practices on data engineering
- Knowledge of distributed systems as it pertains to data storage and computing
- Experience building/operating highly available distributed systems of data extraction ingestion and processing of large data sets

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