OVERVIEW

We are seeking a motivated and technically strong Computer Science Intern – Product Information Management (PIM) to join our Biz Tech Operations team. This internship offers a unique opportunity to design and build a foundational Product Information Management (PIM) capability that will serve as a single, trusted source of product data across ALO’s technology ecosystem. 

Today, product information is distributed across multiple systems—including NGC PLM, FDM4 ERP, FDM4 WMS, Shopify, and spreadsheets—resulting in duplicated integrations, inconsistent data, and manual workarounds. As an intern, you will help design and implement a centralized PIM solution built on ALO’s data lake (Amazon Redshift) and supporting data pipelines, enabling all downstream systems, services, and teams to reliably source product data from a single platform. 

This work will create a scalable foundation that can be expanded over time with additional product attributes, governance, and automation to support ALO’s continued growth and tech environment expansion.

The ALO internship programs starts June 8th and ends July 31st. Interns will be working 40 hours a week at our Beverly Hills HQ.

RESPONSIBILITIES  

• Partner with Tech, Business, and downstream application teams to help define the vision, scope, and requirements for a centralized Product Information Management (PIM) solution
• Design and implement a foundational PIM data model that consolidates product data from multiple enterprise systems, including: 
  • PLM 
  • ERP
  • WMS
  • Shopify
  • Existing datasets in Amazon Redshift
• Build and enhance data pipelines into the data lake (Redshift) to ensure required product attributes are available, current, and reliable
• Develop SQL-based transformations and views that create canonical product records suitable for consumption by multiple systems and microservices
• Assist in defining data ownership, source-of-truth rules, and attribute-level lineage (e.g., which system owns which product attributes)
• Collaborate with Business Analysts and stakeholders to document product data requirements and translate business needs into technical solutions
• Create and maintain technical documentation, including: 
  • Product data models 
  • Source-to-target mappings 
  • Data flow diagrams 
  • Attribute definitions and dependencies 
• Support downstream consumption of PIM data by helping define access patterns (e.g., database views, exports, APIs, or service interfaces)
• Validate data quality and consistency across systems, identifying gaps, conflicts, and opportunities for standardization
• Learn and apply best practices for data modeling, data engineering, and platform design in a real-world enterprise environment

Qualifications

• Currently pursuing a degree in Computer Science, Data Science, Information Systems, or a related technical field
• Strong working knowledge of SQL and relational databases
• Understanding of data modeling concepts, including entities, attributes, relationships, and normalization
• Experience with or exposure to ETL / ELT pipelines and data integration concepts
• Familiarity with one or more programming or scripting languages such as Python, Java, or C# is a plus
• Interest in enterprise systems, data platforms, and foundational architecture
• Strong problem-solving skills and comfort working with ambiguous and evolving requirements
• Ability to communicate technical concepts clearly to both technical and non-technical stakeholders 
• A strong desire to build systems that will be used and extended by others over time

NICE TO HAVE

• Exposure to Product Information Management (PIM), Master Data Management (MDM), or data governance concepts
• Familiarity with cloud data platforms (AWS, Azure)
• Experience integrating data from SaaS platforms (e.g., Shopify or PLM systems)
• Coursework or projects involving data architecture or enterprise systems

The base pay range for this position is $45/hr-$50/hr which represents the current range for the non-exempt position. Please note that actual pay will vary based on factors including but not limited to location, experience, and performance.