• Partner directly with Account Executives, Account Managers, and front-line leaders to lift performance through hands-on coaching • Observe live calls, diagnose skill gaps, and build individualized coaching plans • Coach core selling behaviors: discovery, qualification, value articulation, objection handling, negotiation, and closing • Identify what top performers consistently do differently and scale those behaviors across the team • Own and continuously improve onboarding, ramp, and certification so new hires reach productivity faster • Evolve sales playbooks, messaging, and competitive positioning as our product and market change • Create simple, practical resources that reps actually use in live customer conversations • Define success metrics that connect coaching and enablement to seller behavior and business outcomes • Use data to refine programs, spot trends, and improve execution

• Help us choose the tech stack, with a focus on how cleanly each option integrates with HubSpot • Design how our tools work together, and advise on where to simplify or add • Build and refine our outbound sequences • Keep our enrichment and data flows clean and reliable • Manage HubSpot day to day - workflows, reporting, and data quality • Document how everything works so it stays easy to maintain

• Build and optimize GTM workflows and processes, with a strong focus on top-of-funnel operations. • Manage and maintain our HubSpot instance - pipelines, automations, reporting, data hygiene. • Build and maintain enrichment and outbound workflows using Clay. • Set up and manage integrations between our GTM tools. • Use Claude / Claude Code to build internal tools, automations, and process efficiencies. • Own end-to-end implementation of new tools and processes as we scale. • Work closely with Marketing, Sales, and Customer Success to keep GTM strategy aligned.

• Analyse the customer journey from acquisition and conversion to retention and revenue. • Define and maintain consistent metrics across marketing, sales, customer success, product, and finance. • Extract, combine, clean, and validate data from operational and analytical systems. • Investigate changes in customer behavior, funnels, conversion rates, retention, costs, and revenue. • Determine the likely causes of performance changes rather than only describing the outcome. • Review existing reports and identify unreliable metrics, missing data, and conflicting definitions. • Develop hypotheses and test them against the available data. • Distinguish evidence from assumptions, correlations, and opinions. • Estimate the expected impact of proposed changes and define how success will be measured. • Prioritise opportunities based on impact, effort, and the reliability of the supporting data. • Present findings and recommendations in a format that supports business decisions. • Build selected dashboards, data models, automations, and process improvements. • Measure implemented changes and assess whether they produced the expected results. • Use AI assistants as a daily tool for research, data exploration, coding support, and documentation. • Verify AI-generated results against the underlying data and original sources. • Document metric definitions, data logic, assumptions, methods, and limitations.