Responsibilities

• GTM Systems & Data Foundation
• Own Nametag’s HubSpot instance end-to-end: architecture, pipeline stages, deal properties, workflows, reporting, integrations, and data hygiene.
• Define and enforce CRM standards across Sales and Marketing so GTM data is clean, consistent, and actionable.
• Build and maintain integrations between HubSpot and the broader GTM stack: outbound, enrichment, sequencing, sales intelligence, website analytics, forecasting, and AI automation.
• Evaluate, implement, and govern new GTM tools and AI capabilities as the commercial stack matures.
• Serve as Nametag’s internal expert on HubSpot and GTM tooling — driving adoption, enablement, and continuous improvement.
• AI-Powered Prospecting & Marketing Collaboration
• Partner with Marketing and Sales to build an intelligent demand engine: firmographic, technographic, intent, and engagement data used to identify high-propensity accounts and buying signals.
• Drive ICP-informed segmentation, territory design, account prioritization, and propensity targeting.
• Design and manage the MQL-to-SQL handoff: lead scoring, routing, SLAs, follow-up accountability, and campaign-to-pipeline feedback loops.
• Operationalize AI-assisted outbound personalization: scalable templates, prompts, workflows, and guardrails that help reps personalize at scale without sacrificing quality, accuracy, or brand voice.
• Sales Intelligence & Process Automation
• Build systems to ingest, synthesize, and surface account intelligence at each funnel stage — discovery, demo, proposal, RFP, procurement, and close.
• Create onboarding and enablement frameworks that help new AEs ramp quickly and adopt GTM systems from day one.
• Revenue Insights, Forecasting & GTM Strategy
• Own the weekly and monthly forecast process in partnership with AEs and Sales leadership.
• Build dashboards and reporting cadences that give leadership real-time visibility into pipeline health, funnel conversion, sales productivity, and revenue performance.
• Track and report core KPIs: pipeline creation, MQL-to-SQL conversion, stage velocity, win rate, sales cycle length, ACV, and AE productivity.
• Identify pipeline gaps, coverage risks, conversion bottlenecks, and expansion opportunities before they become problems.