Digital Commerce Data Platform Engineer

at  PepsiCo

Overview:
This job will be encouraged to be in NY however would be acceptable to work at one of the hub locations for digital commerce as well. This includes the offices in Palo Alto and Oakland California as well as Plano, Texas

BUILD DATA FOUNDATIONS THAT DRIVE INNOVATION

Our team at PepsiCo Digital Commerce works with the most cutting-edge technology and builds our own tech-stack from scratch. This is where you’ll see your work come to life, as a member of a small, agile, highly-skilled team that ushers global projects through from conceptualization to implementation.
We’ve been laser focused on winning in the marketplace, developing and executing direct-to-consumer experiences like PantryShop.com and Snacks.com in less than 30 days. At PepsiCo, we leverage a combination of technology, insights, resources and inventory to quickly meet consumers’ evolving needs, with $3 billion in retail sales and growing.
With a team of more than 250 associates and offices in Manhattan, Chicago, Plano, and Silicon Valley, we’re unleashing the full potential of automation, data science, and machine learning to challenge the way snacks and beverages are sold every day.
Responsibilities:
We’re looking for a hands-on engineering manager to join our growing ecommerce team. You’ll have a once-in-a-generation opportunity to influence the biggest players in online grocery and build the data foundation driving one of the world leaders in consumer-packaged goods.

• Scaling the Data Platform to 700+ pipelines across AWS and Azure
• Collaborate on cross-team Data Platform initiatives, like Data Mesh and support international expansion
• Build tools and libraries that ease new pipeline development and reduce delivery time
• Work towards increasing the robustness and fault tolerance on platform processing large amount of data
• Implement best practices and design patterns for different data solutions.
• Build mechanisms to implement metrics that measure data quality and reliability
• Define tools and techniques to improve overall data observability and discoverability
• Bring innovative thinking to solve day-to-day problems faced by data teams.