Marketing Data Analyst

About

This role involves leveraging existing data science models—developed by a separate data science team—to design, execute, and optimize marketing campaigns. The analyst will focus on translating model outputs into actionable marketing strategies, helping the business understand how to use data to improve campaign effectiveness and targeting.

Responsibilities

• Translate marketing and business needs into analytical requirements E.g., data, models required to build strategic analytical solution
• Establish the analytical framework to successfully deliver business OKRs
• Design a robust and streamlined data-lead strategy to drive marketing optimization at scale
• Run BAU, proposed and other “what if” scenarios assessments to validate approach and evaluate impact
• Design an actionable marketing operations and experimentation roadmap
• Build consensus and influence senior stakeholders to gain confidence in analytics proposals
• Continuously evangelize analytical and data-driven problem solving, strategic mindset & experimentation best practices across the organization

Qualifications

• Bachelor’s degree and industry certification preferred, 8+ years’ experience and relevant skillset in the subject matter required, has extensive knowledge of subject matter and extensive knowledge of the business

Key Focus Areas:

• The analyst will work within the Marketing Analytics team, which supports multiple business partners across various marketing functions.
• A solid understanding of marketing analytics concepts, campaign creation, and performance optimization is essential.
• Experience with forecasting, data-driven decision-making, and campaign performance measurement will be highly valuable.

Technical Requirements:

• Proficiency in SQL for data extraction, manipulation, and analysis.
• Working knowledge of Python for data analysis and automation.
• Strong analytical and communication skills to collaborate with marketing stakeholders and translate technical insights into business outcomes.
• While domain experience isn't important these resources need to have supported large enterprises, worked with large sets of data and supported big data platforms (AWS or Azure)