Job Requisition ID #26WD99699
Position OverviewWe 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.
Responsibilities- 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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