Data Engineer, Sales Data Services

- 3+ years of data engineering experience
- 4+ years of SQL experience
- Experience with data modeling, warehousing and building ETL pipelines
- Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
- Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, column-family databases)

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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 , fast APIs and agentic soutions. 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.

Key job responsibilities
Design, implement, and support data warehouse / data lake infrastructure using AWS big data stack, Python, Redshift.
• Develop and manage ETLs to source data from various data lakes and create unified data model for analytics and reporting
• Empower technical and non-technical, internal customers to drive their own analytics and reporting (self-serve reporting) and support ad-hoc reporting when needed.
• Develop deep understanding of vast data sources and know exactly how, when, and which data to use to solve particular business problems.
• Manage numerous requests concurrently and strategically, prioritizing when necessary collaborate across teams to deliver results.
• Mentor other engineers, influence positively team culture, and help grow the team.