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 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 youre interested in joining a rapidly growing team working to build a unique world-class advertising group with a relentless focus on the customer youve 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.
Youll 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 teams 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 teams 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 teams 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 teams 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 AIs 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.

- 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

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

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