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.

• Drive data engineering for business-critical applications, including dashboards and other internal self developed software products.

• Establish and enforce data governance, quality, and reliability standards, implementing monitoring, alerting, SLAs, and data quality frameworks across the team’s data assets.

• Partner cross-functionally with product managers, data scientists, BI engineers, and software engineers to translate business requirements into scalable data architecture and pipeline solutions.

• Represent data engineering in planning and leadership forums, contributing to annual planning, QBRs, and roadmap reviews. Communicate trade-offs, capacity constraints, and investment needs clearly to senior leadership.

• Drive operational excellence, owning on-call processes, incident response, COE follow-ups, and continuous improvement of the team’s operational posture.

A day in the life
Sales Data Services team is part of Sales AI, a central data and science organization within Sales Intelligence, Technology, & Enablement organization (SITE) that powers Ad Sales selling motions and workflows via a suite of AI/ML services. Sales AI’s mission is to equip Sales with the data- and science-backed intelligence they need (and when they need it) to improve their selling motions from routine practices to peak performance.

Basic Qualifications

- 5+ years of processing data with a massively parallel technology (such as Redshift, Teradata, Netezza, Spark or Hadoop based big data solution) experience
- 5+ years of relational database technology (such as Redshift, Oracle, MySQL or MS SQL) experience
- 5+ years of data engineering experience
- 2+ years of developing and operating large-scale data structures for business intelligence analytics (using ETL/ELT processes) experience
- Experience managing a technical team
- Experience communicating to senior management and customers verbally and in writing
- Experience leading and influencing the data or BI strategy of your team or organization
- Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS

Preferred Qualifications

- Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
- Experience with AWS Tools and Technologies (Redshift, S3, EC2)

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