Data Engineer, Data Engineering, Sciences and Analytics; DESA)

Position: Data Engineer, Amazon Live Data Engineering, Sciences and Analytics (DESA)
Description Amazon Live is building the future of shoppable video — connecting brands with customers through live streams, 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…