BI Analyst, Revenue Analytics- Moneylion

Gen•New York, NY

3d•$70,000 - $85,000•Hybrid

About The Position

We are looking for a Business Intelligence Analyst to join our Business Intelligence (BI) team within the Revenue Analytics function. In this role, you will be at the intersection of data, revenue strategy, and financial insight — someone who can turn complex data ecosystems into sharp business intelligence that drives growth, profitability, and operational excellence. You will play a pivotal role in enabling our Revenue, Finance, and Growth teams to make faster, smarter, and more confident decisions. From building dynamic dashboards to creating revenue forecasts, you will shape how we measure performance across the company. This is not a back-office analytics role. You will have a seat at the table in a fast-growing fintech shaping the future of financial access. If you’re excited by the idea of owning the analytical engine that powers financial decision-making and growth strategy at a leading fintech, we would love to hear from you.

Requirements

Bachelor’s degree in a quantitative, analytical, or business-related field (e.g., Data Science, Statistics, Economics, Business Analytics, Engineering, or Finance
Genuine passion for financial services, technology, and business problem-solving, with the ability to translate complex financial or accounting concepts into clear analytical insights
Hands-on experience supporting Revenue, FP&A, or Growth teams, with familiarity in revenue analytics, forecasting, cohort analysis, or unit economics
Proficiency with modern BI tools — previous experience with Looker and LookML is a strong plus (or similar tools such as Tableau, Power BI)
Naturally curious, investigative mindset — you actively listen, probe thoughtfully, and dig beneath the surface to uncover the true drivers behind a problem
Practical judgment and adaptability — you understand when “perfect” is needed and when “good and fast” accelerates the business, and you know how to balance data rigor with rapid execution
1+ years in an analytical, data-focused role, ideally within fintech, financial services, or a high-velocity digital environment
Advanced SQL skills and comfort working within modern data ecosystems (Snowflake, BigQuery, dbt, Looker, etc.)
Strong understanding of data modeling and data architecture principles, and the ability to work efficiently with large, complex datasets
Exceptionally data-driven — you support recommendations with robust analysis, clear logic, and high-quality evidence
High attention to detail, even amidst shifting priorities, ambiguity, and fast turnaround times
Solid foundation in statistics and experience applying statistical methods to real business problems
Excellent communication and storytelling skills, with the ability to translate technical insights into actionable recommendations for stakeholders across Revenue, Finance, Product, and Engineering
Collaborative team player who thrives in a cross-functional environment, acting as a central hub for insights across the organization
Continuous learner with a “why-driven” approach — you consistently question assumptions, validate hypotheses, and push for deeper understanding

Responsibilities

Serve as the primary BI partner for Sales, Revenue Operations, and Finance, delivering high-quality reporting, analytics, and strategic insights that drive growth, efficiency, and margin optimization.
Collaborate with a high-performing team of analysts on cross-functional initiatives spanning financial modeling, data infrastructure, investigative analytics, and revenue optimization.
Leverage Excel, SQL, and Looker to produce rapid ad-hoc analyses and build planned dashboards that illuminate company, product, and customer performance in real time.
Respond to data inquiries from teams across the organization, including executives and senior leadership, translating questions into structured analytical approaches.
Design, build, and maintain automated, intuitive Looker dashboards that convert complex datasets into clear KPIs and actionable insights for stakeholders.
Monitor key dashboards, processes, and data pipelines, providing daily/weekly briefs and proactively identifying potential issues or anomalies.
Identify, operationalize, and automate manual data workflows, partnering with Data Engineering where appropriate to improve speed, accuracy, and scalability.
Own and standardize core business metrics, ensuring alignment across teams and maintaining clear documentation of definitions, logic, and data sources.