Every strikethrough price, reference price, and savings number customers see on Amazon depends on data systems that must be fast, accurate, explainable, and available across the world. This role owns the data foundation behind those experiences. You will design the pipelines, data lakes, and quality mechanisms that help Amazon tell customers when an offer is a good value, power AI shopping experiences with trusted pricing data, and give business leaders a reliable view of how pricing experiences are working at Amazon scale.
The scale is rare. Your systems feed shopping detail pages, search, cart, deals, international marketplace launches, finance analysis, and executive reviews across tens of marketplaces and billions of customer impressions a day. The leverage is rarer still. A schema choice, freshness contract, or quality standard you set becomes the way many teams across Pricing build, consume, and reason about reference-price data.
We are looking for a senior data-engineering owner who wants to build, not just operate. The next twelve months include modernizing legacy pipelines onto a more flexible platform, creating new customer-impression datasets, improving data discoverability and lineage, and setting the next-generation organizational data architecture for how Pricing teams build and consume reference-price data. You will also help lead the transformation of data engineering in Pricing into the agentic AI era, using AI agents for migration, validation, and operational leverage while keeping human judgment where architecture, data contracts, and quality bars matter most.
Key job responsibilities
- Design, build, and operate production-grade data pipelines for reference prices, strikethrough prices, savings data, and customer-impression datasets across worldwide marketplaces.
- Build solid data lakes that power S-Team, leadership, finance, product, and analytics requests on reference-price topics, with discoverability, lineage, and quality checks designed in so BIEs and analysts can answer questions quickly and reproducibly.
- Set the data architecture for how upstream pricing signals are transformed into trusted downstream datasets used by shopping experiences, AI features, executive analytics, and ad-hoc deep dives.
- Lead cross-team data contracts across Pricing, Search, shopping detail pages, Deals and Promotions, international marketplaces, and partner teams that consume reference-price data.
- Modernize legacy data pipelines onto modern data platforms, including cutover design, validation strategy, backfill planning, operational readiness, and deprecation of redundant datasets.
- Raise the operational bar for data freshness, completeness, data-quality monitoring, and incident response across high-scale pricing datasets.
- Mentor data engineers and software development engineers on data modeling, pipeline design, migration patterns, and high-quality operational mechanisms.
- Lead the transformation of data engineering across the Pricing organization into the agentic AI era. Define when agents should handle rote migration or validation work, how engineers verify those outputs, and how proven patterns become team-wide standards.
A day in the life
You start the morning deep in a design doc, sketching the schema for a new reference-price dataset and writing the freshness contract that downstream teams will build against. Late morning, you ship a small but high-leverage code review that removes duplicate logic from an existing pipeline. You head out to lunch with the software engineering team.
Back at your desk, you kick off an AI agent to migrate a legacy pipeline onto the modern data platform, then settle into a focused block on the harder pieces of the same modernization: the cutover plan is written, validation jobs are running, and the first batch lands for a downstream consumer to sanity-check. You close the day landing a new dataset version that the BIE team will turn into the weekly business-review slide for an SVP read.
About the team
Price Perception and Evaluation owns the customer-facing pricing experiences that help customers understand value on Amazon, including reference prices, strikethrough prices, savings messages, and the data foundations behind those experiences. We sit at the intersection of pricing, shopping customer experience, data engineering, product, and analytics. Our work has unusually broad reach because the same data foundation serves production shopping surfaces, AI shopping features, business analytics, and leadership inspection.
We are a builder-heavy team with a high bar for technical judgment. We value simple architectures, strong ownership, precise data definitions, and mechanisms that make the right thing easy for partner teams. We are also actively changing how data engineering work gets done by using AI agents to accelerate migrations and operations while keeping engineers focused on design, correctness, and long-term maintainability.
