At Shutterfly, we make life’s experiences unforgettable. We believe there is extraordinary power in the self-expression. That’s why our family of brands helps customers create products and capture moments that reflect who they uniquely are.

Position Summary:
As part of the Data Engineering team you will tackle the scalability, performance and distributed computing challenges needed to collect, process and store data for a $2B customer eCommerce, images and customizable product business. You will enhance the Data Warehouse at Shutterfly on AWS, supporting the breadth and depth of the company’s analytic needs, including the BI, Data Sciences, Product Management, Product Marketing, CRM, and Machine Learning teams to deliver data innovations in our Websites and Mobile Apps.

The Data Warehouse, Platform and Infrastructure vision is to provide Shutterfly teams the ability to manage the full life cycle of their data at all levels, simplifying, commoditizing and democratizing its collection, computation and analytics through well-architected use of the AWS.

To apply for this role, we are looking for candidates with sound analytic, design and problem-solving skills, who have expertise with distributed and high-performance systems, service design and large-scale data ingress, egress and storage. Expertise with the AWS platform & Databricks is needed.

What You'll Do Here: 
• Own & build design, develop, test, deploy, maintain and enhance full-stack data engineering solutions for the Data Pipelines & Data Mart encompassing the Data Warehouse
• Provide technical leadership to both internal Data Warehouse team as well as to publishers & subscribers of the Shutterfly’s Enterprise Data Lake
• Identify, evaluate and evangelize through data-based evidence improvements to the Data Lake as we as the data processing environment; hence influence the data strategy
• With your technical expertise, own and manage project priorities, deadlines and deliverables
• Always with a customer focus, evangelize the benefits of existing solutions and new technologies to drive the use and push the technology of the Data Warehouse forward
• Work closely with Data Operations to improve CI/CD pipelines, as well as continually improve the operations, monitoring and performance of the Data Warehouse
• Work across multiple teams in high visibility roles and own solutions end-to-end

The Skills You'll Bring: 

  • Expert knowledge Python, Spark and SQL scripting, data modeling
  • Deep understanding of data warehouse, data lake concepts
  • 10+ years of hands-on experience in building data & feature engineering applications, including design, implementation, debugging, and support
  • Deep understanding of data integration to support analytics & feature engineering for Machine learning algorithms
  • Strong at applying data structures, algorithms, and object-oriented design, to solve challenging data integration problems
  • Experience working in the AWS Services Ecosystem or relevant Cloud Infrastructures such as Google Cloud or Azure
  • Experience with Databricks, AWS Glue as a compute environment
  • Bachelor’s / master's degree in computer science or equivalent

Supporting a diverse and inclusive workforce is important to Shutterfly not only because it directly reflects our value of Embracing our Differences, but also because it’s the right thing to do for our business and for our people. Learn more about our commitment to Diversity, Equity and Inclusion at Shutterfly DE&I.

The compensation package for this role is based on multiple factors, such as job level, responsibilities, location, and candidate experience. The base pay ranges included below are specific to the locations listed, and may not be applicable to other locations. California : [ $138,100-197,250] Connecticut, New York, and Rhode Island: [$138,100-179,100] Colorado and Washington: [$138,100-165,600] Nevada: [$129,800-179,100]  This position may be eligible for a bonus incentive, health benefits, a 401K program, and other employee perks. More details about our company benefits can be found at