San Francisco, CA, USA, United States of America
Onsite
Job Requisition ID #
Position Overview
We are seeking a Senior Business Analytics Manager to support Go-to-Market Finance organization by driving data-informed strategy, building scalable analytics solutions and AI-enabled workflows that improve planning, forecasting and reporting. This role will play a critical part in accelerating time to insights, reducing manual work, and shaping how Finance uses data, automation, and AI to make better business decisions.
As a senior member of the Business Analytics function, you will lead complex, cross-functional initiatives across Finance, Sales, Data Engineering, and business operations. You will translate ambiguous business problems into structured analytical frameworks, scalable data products, and practical AI-enabled solutions that improve decision quality and operational efficiency.
The ideal candidate can operate at both the strategic and executional level: partnering with senior stakeholders to define the right business problems, while also being hands-on enough to query data, prototype solutions, pressure-test metrics, and partner with technical teams to scale durable products. This is a senior individual contributor role that requires strong business judgment, technical depth, product thinking, and the ability to influence and scale solutions across teams.
Lead end-to-end analytics and AI-enabled workflow initiatives, from problem framing and stakeholder alignment to solution design, insight generation, adoption, and measurable business impact
Partner closely with Finance stakeholders to understand planning, forecasting and reporting workflows, and identify opportunities to improve them through data, automation, and AI
Translate ambiguous Finance and business challenges into structured analytical frameworks, including data modeling, KPI design, forecasting, experimentation, and decision-support workflows
Design and build scalable data products, including pipelines, tables, dashboards, reporting layers, and AI-ready datasets that enable automated, flexible, and action-oriented Finance analytics
Build and test prototypes for AI-enabled Finance workflows, such as automated business review narratives, anomaly detection, variance explanation, forecast drivers, natural-language insight discovery, and self-service reporting
Partner with Data Engineering, Finance Systems and central data teams to move prototypes into reliable, governed, production-ready solutions
Define and standardize KPI frameworks for financial metrics ensuring alignment across Finance and business teams
Design and scale dashboards, reporting solutions, and self-service analytics experiences that improve adoption, reduce manual reporting, and help business partners make faster, more consistent decisions
Analyze business and financial metrics to uncover key drivers of growth, retention, churn, productivity, and efficiency, and recommend clear data-backed actions
Establish scalable, repeatable analytics frameworks and best practices that improve Finance decision-making and create consistency across planning, forecasting, reporting, and business reviews
Apply strong governance, quality control, and documentation practices to ensure AI-enabled workflows are accurate, explainable, secure, and appropriate for Finance decision support
Communicate insights, recommendations, and roadmap tradeoffs to senior leadership through clear storytelling, executive-ready presentations, and concise written narratives
Mentor junior analysts and contribute to raising the overall analytical, technical, and AI-readiness capabilities of the Business Analytics team
Minimum Qualifications
Bachelor’s degree or higher in a quantitative field
8-10+ years of experience in analytical fields such as consulting, strategic finance, BizOps, data science, business intelligence, data engineering
Advanced SQL skills with the ability to work with large, complex datasets and multiple data sources
Strong experience with BI and visualization tools such as Power BI, or similar platforms
Experience partnering with Finance, GTM, or business leadership teams to support planning, forecasting, reporting, and performance management
Demonstrated ability to structure ambiguous problems, perform deep analysis, and translate findings into clear business impact
Experience designing scalable dashboards, reporting solutions, data models, or analytics products that improve self-service and reduce manual work
Strong stakeholder management skills, with experience influencing senior business partners and driving alignment across cross-functional teams
Excellent communication skills, with the ability to distill complex analysis, technical concepts, and business tradeoffs into clear, actionable recommendations
Proven ability to lead cross-functional initiatives and deliver results in a matrixed environment
Preferred Qualifications
Experience supporting Go To Market, SaaS, or subscription-based business models
Experience building analytics, automation, or AI-enabled workflows for Finance, FP&A, GTM Finance, Sales Finance, or business operations teams
Experience with Python or other statistical tools for advanced analytics, modeling, or experimentation
Experience applying AI, machine learning, or generative AI capabilities to business workflows, such as automated narratives, anomaly detection, forecasting, natural-language analytics, document intelligence, or decision-support tools
Experience designing AI-ready data products, semantic layers, metric stores, or governed datasets that support self-service analytics and workflow automation
Experience building and deploying predictive models to forecast key metrics
Experience designing an optimal decision support process, defining a product strategy to enable it, translating into a roadmap, and enabling it through technology
Domain experience in FP&A and / or sales, marketing, and customer success operations
Experience working in high-growth or transformation environments
Strong understanding of responsible AI practices, including data quality, human-in-the-loop review, model limitations, explainability, and governance considerations
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