Data Engineering Manager, Sales Data Services (SDS)

Amazon Advertising operates at the intersection of eCommerce and advertising, offering a rich array of digital display advertising solutions with the goal of helping our customers find and discover anything they want to buy. We help advertisers reach Amazon customers on Amazon.com, across our other owned and operated sites, on other high-quality sites across the web, and on millions of Kindles, tablets, and mobile devices. We start with the customer and work backwards in everything we do, including advertising. If you’re interested in joining a rapidly growing team working to build a unique, world-class advertising group with a relentless focus on the customer, you’ve come to the right place. SDS is part of the Sales Intelligence, Technology, & Enablement organization (SITE). We are a big-data focused engineering team that provides unique, pan-Amazon datasets consolidating advertiser, seller, and vendor KPIs, exposed via data warehousing , data APIs and agentic solutions. Our data platform is designed to serve Sales teams across WW Advertising orgs for various applications including business processes, ML, account management, marketing, and decisioning systems. You’ll manage a team of data engineers and software developers, own the data infrastructure, pipelines, and platform capabilities that power our analytics, AI tools, and business-critical applications. This requires you to balance competing priorities, manage complex stakeholder relationships, and work shoulder-to-shoulder with product managers, data scientists, and software developers to deliver scalable data solutions at pace.

Key job responsibilities

• Lead and develop a team of data engineers and SDEs, hiring, mentoring, setting technical direction, managing performance and growth opportunities.

• Own the data platform strategy and roadmap for the team’s portfolio, including multiple Redshift clusters, ETL/ELT pipelines, and data models that serve analytics, data science, and business stakeholders.

• Manage and optimize Redshift, druid and low-latency retrieval infrastructure, ensuring performance, cost efficiency, availability, and scalability across multiple clusters serving diverse business lines.

• Own data pipelines for AI-powered tools, ensuring reliable, high-quality data flows that power the team’s GenAI and ML applications including content analysis, audience segmentation, and experimentation automation.