Marketing Analytics Intern

IDeaS•, Remote

13h•Hybrid

About The Position

The Marketing Data & Operations Intern will support foundational projects connecting marketing strategy, customer engagement data, and scalable reporting. Working with marketing and data stakeholders, the intern will translate business questions into documented requirements, organized data structures, prototypes, and recommendations. The role is intended for a curious, analytical student who is comfortable learning new tools and addressing open-ended problems. The primary project focuses on a cross-channel attribution and influence model. The intern will inventory marketing and sales interaction sources; help define interaction types, business rules, required fields, and success criteria; assist with mapping and normalizing data across platforms; support a prototype or proof of concept for attribution reporting; test outputs; document findings and limitations; and recommend implementation and maintenance steps. Potential supporting projects include organizing and documenting marketing data in AWS, researching persona- and journey-based segmentation, and documenting data hygiene practices and processes. The intern will gather requirements, analyze source data, create project documentation, validate results, communicate progress and recommendations, and prepare a final presentation and reusable documentation. The internship is designed to develop the ability to structure cross-channel marketing interactions for analysis, translate business questions into data requirements, document and test practical solutions, and communicate recommendations to stakeholders. Depending on project scope and readiness, the intern may gain applied experience with AWS, SQL, Salesforce, HubSpot, segmentation, customer journey concepts, and marketing measurement.

Requirements

Currently pursuing a degree in Marketing Analytics, Business Analytics, Data Analytics, Information Systems, Computer Science, Data Science, Marketing, or a related field.
Strong analytical thinking, curiosity, attention to detail, and ability to learn unfamiliar systems.
Experience with spreadsheets.
Ability to communicate clearly, document work thoroughly, and collaborate across business and technical perspectives.
Must be legally authorized to work in the United States and must not require sponsorship for employment visa status now or in the future.

Nice To Haves

Third-year student preferred.
Experience with SQL, data modeling, cloud platforms, CRM, marketing automation, or visualization tools is helpful but not required.
Equivalent combination of education, training, and relevant experience may be considered in place of the stated education requirement.

Responsibilities

Inventory relevant marketing and sales interaction sources and document the data available from each source.
Help define and document interaction types, business rules, required fields, and success criteria for an attribution and influence model.
Assist with mapping and normalizing interaction data across platforms into a consistent structure.
Support development of a prototype or proof of concept for business-defined attribution reporting.
Test outputs, document findings and limitations, and recommend next steps for implementation and maintenance.
Support organization and documentation of marketing data in AWS, including source mapping, transformation requirements, and downstream use cases such as making useful outputs available to Salesforce.
Research persona- and journey-based segmentation concepts, identify potential persona variables and channel journeys, connect academic frameworks to the business, and recommend a practical segmentation approach.
Document repeatable data quality practices, segmentation rules, definitions, and team guidance as project needs evolve.
Gather and document business and data requirements through stakeholder conversations.
Analyze source data structures and identify gaps, inconsistencies, or dependencies.
Create clear documentation, process maps, data dictionaries, or source-to-target mappings.
Use structured analysis to test assumptions and validate results.
Communicate progress, risks, findings, and recommendations in a concise, business-friendly format.
Prepare a final presentation and reusable project documentation.