Shape how we acquire and grow customers by turning digital signals into clear, measurable actions. You’ll lead performance analytics across key marketing and product journeys, partnering closely with teams to improve conversion efficiency and long-term value. This role blends hands-on analytics with strong business judgment—prioritizing what matters, establishing standards, and accelerating delivery through smart automation. If you’re energized by connecting customer behavior to business outcomes, you’ll find meaningful impact and growth here. 

Job summary 

As a Vice President, Marketing Analytics Manager in the Marketing Analytics team, you’ll help us understand and improve the end-to-end digital acquisition journey—where customers engage, where they drop off, and what drives conversion and funding outcomes. You’ll translate complex channel, behavioral, and customer-level data into practical recommendations that improve performance and customer experience. You’ll set measurement standards and build repeatable analytical frameworks that scale across channels and journeys. You’ll collaborate with partners across Marketing, Digital Product, and Technology to deliver insight with speed, quality, and clear controls. 

You will also help modernize how marketing analytics is delivered—strengthening data management, improving operational reliability, and using automation (including approved artificial intelligence-assisted approaches) to reduce manual effort and increase consistency. You’ll contribute to “human-in-the-loop” operating models by defining quality checks, monitoring signals, and explainability expectations so insights and execution can scale responsibly. 

Job responsibilities 

• Lead digital channel performance analytics across owned and paid channels, monitoring acquisition, engagement, conversion, and funding outcomes.
• Develop and maintain funnel analytics frameworks to pinpoint drop-offs and prioritize conversion opportunities across journeys.
• Partner with Digital Product and Marketing teams to assess customer experiences across acquisition paths and recommend improvements that increase conversion efficiency.
• Establish performance measurement standards and key performance indicator frameworks spanning impressions, clicks, applications, approvals, account openings, funding, and customer value.
• Leverage behavioral, channel, and customer-level data to uncover drivers of performance and optimize targeting, creative strategy, and channel investment.
• Design and deliver test-and-learn measurement approaches (including attribution and journey measurement) to quantify impact and inform decisions.
• Build repeatable datasets and automation-friendly outputs to accelerate reporting, analysis, and insights delivery.
• Develop and implement data management strategies for marketing execution, including automated operational reporting and data quality monitoring.
• Implement proactive issue detection (alerts and anomaly flags) to protect delivery, measurement integrity, and performance.
• Collaborate with Product and Technology to define requirements for platform enhancements and system implementations, with measurable outcomes and clear controls.
• Quantify and communicate value delivered (for example, cycle-time reduction, hours saved through automation, quality improvements, and performance lift).

Required qualifications, capabilities, and skills 

• Bachelor’s degree in Data Science, Statistics, Information Systems, or a related field.
• Experience delivering digital marketing analytics and funnel measurement, including conversion optimization and web/app analytics.
• Ability to translate complex analyses into actionable recommendations that improve acquisition performance and business outcomes.
• Proficiency with data querying and analysis tools such as SQL and Python.
• Experience using analytics and visualization tools such as Excel and Tableau (or equivalent).
• Familiarity with digital analytics platforms such as Adobe Analytics (or equivalent enterprise measurement solutions).
• Demonstrated ability to build repeatable datasets, standardized outputs, and automation-friendly reporting.