Marketing & Ops Data Analyst

Risepoint

About The Position

The Data Analyst is a hands-on builder and trusted partner within Analytics Solutions, the data, insights, reporting, and AI analysis layer powering marketing and operations decisions across the COO organization. The role turns data from first consideration and advertising spend through enrollment and student outcomes into clear reports, dashboards, and insights for Channel Marketing, Integrated Marketing, Student Success, and Strategy teams. The analyst will grow alongside the organization’s shift to AI-enabled analytics, delivering accurate and responsive work in a fast-paced environment where service and speed matter. The analyst independently manages assigned reporting and analytics deliverables, builds and maintains reports, datasets, and metrics connecting advertising spend and funnel performance, and develops Power BI dashboards and recurring reports. The position also involves SQL and data-layer optimization, contributing to Databricks data models and pipelines, exploring AI-powered analytics, investigating performance changes, supporting forecasting and measurement work, communicating findings, and protecting data quality across source systems.

Requirements

Bachelor's degree in Economics, Mathematics, Statistics, Finance, Operations Research, Engineering, Marketing, Business, or a related field.
At least 2 years of experience in marketing analytics, business analytics, or a related analytical role.
Proficiency in SQL for querying, joining, and transforming data; CTEs and window functions are a plus.
Hands-on experience building reports and dashboards in Power BI or a similar BI tool, with working knowledge of data modeling and DAX.
Strong Excel skills.
Experience analyzing funnel, conversion, ROI, and marketing performance data, and independently performing exploratory analysis to uncover actionable insights.
Strong communication and data storytelling skills, with the ability to present findings clearly to business audiences.
Service mindset and relationship-building skills; ability to collaborate across functions, manage multiple priorities, and meet fast turnaround times.
Curiosity and eagerness to learn new tools and methods, including AI-enabled analytics, and comfort working through ambiguity.

Nice To Haves

Databricks experience, including Delta Lake, PySpark, or notebooks, or experience with other modern cloud data platforms.
Python or R for analysis and automation.
Exposure to AI-enabled analytics, such as LLM-based reporting, automated insights, or natural-language data tools (e.g., Databricks Genie or similar).
Experience with web analytics platforms such as GA4 or Adobe Analytics, clickstream analysis, and CRM platforms.
Exposure to paid media platforms such as Google Ads, Meta, or LinkedIn, attribution, Marketing Mix Modeling, or experimentation.
Exposure to customer research methodologies, survey design, or market analysis.

Responsibilities

Independently manage assigned reporting and analytics deliverables, building and maintaining reports, datasets, and metrics that connect advertising spend and funnel performance for Channel Marketing, Integrated Marketing, Student Success, and Strategy; apply established KPI definitions and flag gaps.
Develop and maintain Power BI dashboards and recurring reports, including data models and DAX, with clear design and documentation; automate manual reporting where possible.
Write and optimize SQL and contribute to data models and pipelines in Databricks, partnering with senior team members to prepare clean, governed data for reporting and AI use.
Help test and scale AI-powered analytics, such as automated summaries, natural-language data queries, and intelligent reporting workflows, and share effective approaches with the team.
Analyze funnel, cohort, channel, and conversion performance; identify likely drivers and choose appropriate analytical approaches to answer defined business questions.
Investigate performance changes and support LTV/ROAS, forecasting, experimentation, and Marketing Mix Modeling work, translating findings into clear recommendations.
Respond to stakeholder requests by clarifying business questions, setting expectations, delivering on time, and communicating findings clearly to business partners and, with guidance, leadership.
Validate and reconcile data across advertising platforms, CRM, and web analytics sources; support tagging and event-data checks and escalate data issues promptly.
Take ownership of the accuracy, reliability, and timeliness of reports and analyses.
Support stakeholder adoption and satisfaction with dashboards and analytics outputs, and respond to requests.
Help reduce manual reporting effort and increase self-service usage.
Build technical skills and take on increasing independent ownership of analytics work.