Marketing & Ops Data Analyst

Role overview Build and maintain reporting and analytics that help marketing and operations teams understand the path from advertising and initial interest through application, enrollment, and student outcomes. You’ll combine hands-on dashboard development, data preparation, and business analysis, while helping the team explore AI-supported ways to deliver insights.

Responsibilities - Manage reporting deliverables and maintain datasets and metrics covering marketing spend, leads, applications, enrollment, and related funnel performance. - Develop and document Power BI dashboards and recurring reports, including data models and DAX, and automate manual reporting where practical. - Write and optimize SQL, and contribute to data models and pipelines in Databricks with guidance from senior colleagues. - Analyze changes in funnel, cohort, channel, and conversion results; investigate likely drivers and recommend next steps. - Support lifetime value and return-on-ad-spend analysis, forecasting, experiments, and marketing mix modeling. - Validate data across advertising platforms, CRM, and web analytics; investigate inconsistencies and communicate findings to stakeholders.

Requirements - Bachelor’s degree in a quantitative, business, marketing, engineering, or related field. - At least two years of experience in marketing analytics, business analytics, or a related analytical role. - Proficiency in SQL; familiarity with CTEs and window functions is useful. - Hands-on experience building dashboards or reports in Power BI or a similar BI tool, with working knowledge of data modeling and DAX. - Strong Excel skills and experience analyzing funnels, conversions, ROI, or marketing performance. - Clear communication and data storytelling skills, a service-oriented approach, and the ability to manage multiple requests and deadlines.

Nice to have - Databricks or another modern cloud data platform; Python or R for analysis and automation. - Exposure to AI-enabled analytics, web analytics, CRM, paid media, attribution, experimentation, or marketing mix modeling.