Description Amazon Live is building the future of shoppable video — connecting brands with customers through livestreams, short-form video, and creator-driven content across Amazon Shopping, Fire TV, Prime Video, and social platforms. The product serves millions of monthly viewers, processes billions of events daily across 9 marketplaces, and generates tens of millions of dollars in advertiser revenue, with a goal to reach 100MM+ MAUs and 20K+ brands in the next couple of years. Amazon Live has a mature, high-performing data platform powering reporting across 9 marketplaces, 14+ dashboards, real-time creator analytics, and a growing suite of AI-native tooling (self-serve analytics agents, agentic operational assistants). The opportunity ahead is to build the next layer on that strong foundation: the team is standing up an intelligence layer — causal measurement, content intelligence, and ranking signals — and that requires a new class of foundational, reusable data assets purpose-built for both analytics and ML consumption. This role builds them. We are looking for a Data Engineer to join the DESA team as a dedicated owner of forward-looking data architecture. You will design and build durable, well-modeled datasets that serve three consumers at once: the BIE for executive reporting, the Applied Scientists for model training, and downstream ML systems (ranking, personalization, recommendations) for feature consumption. The goal is leverage — build an asset once, serve many use cases, and let the whole team move faster on top of it. Your work becomes the foundation the science function builds on: when the data assets are ready ahead of need, scientists model from day one instead of waiting on bespoke pipelines. This is a high-ownership role on a team that moves fast. You will go from problem to production quickly, in a challenging and fast-paced environment, and you will work across the full breadth of the data domain — platform engineering, data modeling, ML-ready feature production, data quality, and AI-assisted development. You will partner closely with Applied Scientists, MLEs, fellow Data Engineers, and the BIE, giving you the opportunity to learn and execute across every aspect of a modern data organization — from raw event ingestion through the models and reporting that move multi-million-dollar business decisions. Key job responsibilities - Design and build a content metadata layer that unifies broadcast events, product catalog data, creator profiles, and show metadata into reusable assets consumed by both dashboards and models. - Build cross-session shopper profile datasets that sessionize event data across visits and compute rolling engagement metrics (frequency, recency, depth, conversion lag). - Build integration pipelines connecting retail catalog and advertising performance data into the Amazon Live platform, unlocking new dimensions for analytics and ML. - Produce structured, ML-ready datasets that feed Applied Scientists' models and serve features to downstream ranking and personalization systems. - Own and advance data quality: proactive monitoring and anomaly detection so the team stays ahead of issues. - Advance platform engineering maturity — CI/CD for ETL (BDT/Maestro), standardized deployment, and AI-assisted code generation and review. - Partner with Applied Scientists, MLEs, DEs, and the BIE to align data assets to model, serving, and reporting needs. A day in the life You will move fast across a broad surface. Some weeks you will be designing a denormalized content metadata asset with an Applied Scientist to unblock a new model; other weeks you will be integrating a retail or advertising data source, hardening a pipeline's CI/CD, building data-quality alarms, or packaging features for the distribution team's ranking systems. You will partner daily with scientists, MLEs, DEs, and the BIE — scoping what to build and how it will be consumed — and you will have self-serve analytics agents and agentic operational tooling to accelerate your work (and a hand in extending that tooling as the platform grows). The pace is quick, the problems span the full data lifecycle, and the ownership is real. About the team The DESA team owns the data platform, analytics, and sciences for Amazon Live and Shop the Show. It is a compact, senior, high-trust team: DEs own the infrastructure and foundational data assets, the BIE owns executive reporting, Applied Scientists and MLEs own the models, experiments, and signals that turn data into product value, and everyone collaborates closely across those boundaries. As a Data Engineer here, you own forward-looking data architecture — the durable, reusable foundation the intelligence layer is built on — and you get exposure to the entire data domain, from event ingestion through production ML and leadership reporting. Your datasets become shared org infrastructure: the inputs to attribution models for partner teams, ranking features for distribution, and the reporting the business runs on.