[Remote] Vice President, Data and Business Operations

Note: The job is a remote job and is open to candidates in USA. AgencyBloc is a leading provider of agency management solutions built specifically for life and health insurance agencies. The company is seeking a results-driven leader to establish and head their new Data and Business Operations organization, focusing on AI operationalization and business transformation. This role involves building prototypes, driving workflow re-engineering, and ensuring alignment with the enterprise data strategy.

Responsibilities

• Drive Hands-On Development: Define the operational blueprint, data readiness standards, and execution roadmap for AI adoption across all core business functions (including Customer Success, Support, Sales, Marketing, and Shared Services). This includes the expectation to personally build and iterate on prototypes, configure new AI-driven workflows, and drive rapid development
• Drive Enterprise Data Integration: Oversee the convergence of internal data silos (Data Ops, RevOps, Customer Data) to define business data requirements, data quality expectations, and operational priorities for enterprise reporting, analytics, and AI initiatives in partnership with the Technology organization
• Execution, Tooling & Scale: Lead the vetting, procurement, deployment, and ongoing utilization of business-specific AI tooling and cognitive agents to guarantee high-quality functional execution, cost containment, and organizational scale
• Strategic Barrier Removal: Continuously partner with senior leadership to identify, prioritize, and remove macro-level organizational, technological, and data-quality barriers to enable enterprise-wide AI transformation
• Outcome & ROI Ownership: Hold full accountability for delivering clear, measurable operational outcomes with an offensive growth mindset, focused on maximizing growth and efficiency across the entire business. This includes increasing customer retention (e.g., churn reduction), improving GTM enablement and pipeline velocity (e.g., doubling sales capacity), and driving enterprise-wide capacity/time-to-value acceleration through AI-driven operational excellence
• Establish the AI Center of Excellence (CoE): Establish a Business AI Center of Excellence focused on operational automation, adoption, workflow redesign, change management, training, and operational productivity. Identify systemic gaps in existing legacy workflows and resolve them through strategic process optimization, agentic workflows, and predictive data utilization. This role will drive the initial build and configuration of the operational AI application layer in close partnership with the Technology organization, which holds the ultimate decision authority on tool selection, foundational architecture, and technical standards
• Establish Data Stewardship: Establish business ownership and stewardship models for key enterprise data domains, including customer, revenue, support, and operational data
• Team Establishment & Integration: Hire, structure, and lead the unified Data and Business Operations team, successfully integrating existing fragmented functions such as Revenue Operations (RevOps)
• Product & Technology Partnership: Serve as the primary business liaison between commercial, operational, Product, and Technology organizations to drive enterprise AI adoption and business transformation initiatives. Partner closely with Product and Technology leadership to prioritize opportunities, define business requirements, and ensure successful organizational adoption. This partnership includes advising on architecture, defining business requirements for the application layer, and driving immediate operational implementation, all while working within the framework and technical constraints set by the Technology organization, which retains accountability for data engineering and base infrastructure
• Strategic Portfolio Management: Manage the overall portfolio of internal AI operationalization projects, driving cross-departmental buy-in, establishing clear governance, and leading organizational change management

Skills

• 10+ years of senior leadership experience in Business Operations, Revenue Operations, Data Strategy, or Management Consulting, with a proven track record within a high-growth, private equity-backed SaaS or enterprise technology environment
• Demonstrated success designing, implementing, and optimizing large-scale enterprise operating systems (e.g., Salesforce, modern data warehouses like Snowflake/BigQuery, BI tools) and leading an organization through a fundamental technological pivot
• Highly analytical mind with experience defining, tracking, and holding accountability for EBITDA-impacting metrics, operational efficiency ratios, and ROI benchmarks directly tied to automation
• Demonstrated practical tool mastery of the modern AI landscape and its application within enterprise operations. This includes explicit experience with modern AI orchestrators, agent builders (e.g., Cassidy, MindStudio), and LLM frameworks (e.g., LangChain). Ability to identify high-value business use cases, distinguish practical applications from market hype, and drive measurable operational outcomes through responsible AI adoption
• Demonstrated experience leading enterprise adoption of AI-enabled tools and capabilities across business functions. Proven ability to develop organizational readiness plans, establish usage standards and best practices, drive training and change management programs, and measure adoption, productivity improvements, and business outcomes. Experience partnering with Technology and IT organizations to ensure the successful rollout of AI capabilities while fostering sustainable employee engagement and operational transformation
• Strong understanding of enterprise data strategy, data governance principles, business intelligence, master data management, and data quality management. Ability to establish data ownership, stewardship practices, KPI governance, and business data standards that enable trusted analytics and AI adoption. Experience assessing organizational data readiness and partnering with data architecture and engineering teams to ensure enterprise data assets are accurate, accessible, secure, and fit for purpose

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