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ExaExaSan Francisco, CA

Head of Marketing Operations

Build and scale the marketing operations engine at an AI search company. Own the tech stack, AI-powered automations, lead lifecycle, attribution, analytics, and operating rhythms that connect Marketing, Sales, and RevOps to generate predictable pipeline.

200k – 285k/yr
On-site6+ YOERevenue Operations

About the role

What You'll Own

Marketing technology stack and architecture
Own the marketing tech stack end to end: selection, implementation, integration, and the standards that govern how data flows between systems, including marketing automation, enrichment, and the integration layer with Salesforce. Success is measured by stack reliability, total cost of ownership, and the speed at which marketing can launch new programs.

AI-first automation
Design and deploy AI-powered workflows (Claude, Clay, Zapier, and whatever you build yourself) to automate high-frequency tasks, and evangelize AI adoption across the GTM team. If a process runs manually more than twice, you should be itching to automate it.

Lifecycle, lead management, and scoring
Own the full lifecycle from anonymous visitor through closed-won: lead scoring, MQL and PQL definitions, SLA enforcement, routing, enrichment, nurture, and the handoff to sales. Success is measured by conversion lift and clean handoff metrics.

Attribution, reporting, and analytics
Own marketing analytics, attribution methodology, and the dashboards leadership sees: pipeline attribution, channel ROI, and the quarterly marketing performance review. Success is measured by leadership confidence in the numbers and the speed of decisions informed by them.

Marketing operating rhythm
Drive the drumbeat of the marketing team: quarterly planning, goal tracking, weekly business reviews, budget and vendor management, and the prioritization framework that turns a long list of ideas into a focused roadmap.

Data governance and deliverability
Own data hygiene, privacy compliance (GDPR, CCPA, and emerging US state laws), email deliverability, and the governance model that keeps our database trustworthy as we scale.

What Success Looks Like

  • Marketing-sourced pipeline is reported with confidence and audit-ready definitions.
  • The full funnel from visitor to closed-won is instrumented and visible in shared dashboards.
  • Lead scoring and routing drive measurable lift in downstream conversion.
  • Campaign launch time drops because of better playbooks and automation.
  • Marketing, sales, and RevOps agree on the data, the definitions, and the single source of truth.
  • Leadership has real-time visibility into program status, spend, and performance without chasing updates.

You Should Have

  • 6+ years in marketing operations, with meaningful time at a high-growth B2B startup. You thrive in ambiguity and don't need a playbook to get started.
  • Experience building marketing operations from scratch or replatforming, ideally at a developer-first or PLG company.
  • Deep fluency in HubSpot or Marketo, Salesforce, and at least one BI tool (Looker, Tableau, Sigma, Hex).
  • A proven AI builder mindset: you've shipped automations and internal tools using LLMs and workflow platforms (Clay, Zapier, Make, n8n), not just used ChatGPT.
  • Track record owning attribution methodology, lead scoring, routing, and data quality at scale.
  • Experience running planning cadences, budget, and vendor management alongside a marketing leader.
  • Impeccable analytical and problem-solving skills; calm under pressure, strong opinions held loosely.

Nice to Have

  • SQL fluency and experience with ETL/data pipelines or warehouse-native architecture.
  • Experience with hybrid PQL + MQL scoring models.
  • Familiarity with the developer tools landscape and how developers like to be marketed to (hint: they don't).
  • Bachelor's degree in a quantitative or technical field (or equivalent hands-on experience).
  • Experience managing outbound infrastructure and deliverability at scale.

Skills

marketing operationsHubSpotMarketoSalesforceLookerTableauSigmaHexAI AutomationClaudeClayZapierLLMslead scoringattribution modeling
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