Position Overview

We are seeking a Senior Business Analytics Manager to supportGo-to-Market Finance organizationby 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 manager in the Business Analytics function, you will lead a team responsible for 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

• Manage, coach, and develop analysts and contribute to raising the overall analytical, technical, and AI-readiness capabilities of the Business Analytics team

Minimum Qualifications

• Bachelor27s 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 manage analysts, 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