Marketing Analytics Director | United States | Remote

Grafana Labs Grafana Labs · Data AI · United States · Remote · Marketing

Marketing Analytics Director to lead the evolution of the marketing data stack and raise analytical rigor. This role involves building systems, including AI agents, and leveraging Google BigQuery, Grafana, and agentic AI to create a unified source of truth. Responsibilities include architecting target-setting frameworks, designing predictive models, implementing BI frameworks, and translating data into executive direction. The role will deploy LLM-powered agents for monitoring datasets, implement orchestration patterns for automated workflows, and build AI-driven scoring models.

What you'd actually do

  1. Serve as the primary strategic partner to the GTM Leadership team (VP of Demand Gen, VP of Regional and Events, Head of Marketing Ops, CMO, Revenue Operations, Sales leadership and more), translating complex data into a clear roadmap for demand generation and revenue growth.
  2. Architect and own a sophisticated forecasting framework that balances top-of-funnel volume with high-intent lead quality, optimized for Grafana Cloud conversion and retention.
  3. Develop and maintain machine learning models (Attribution, MMM, LTV) to predict campaign impact and steer budget allocation toward the highest-ROI channels.
  4. Oversee the structure of marketing data within Google BigQuery, ensuring a scalable single source of truth that connects product usage data with marketing touchpoints.
  5. Transform technical data outputs into clear, compelling narratives for the executive team and board. Deliver a succinct read and go deep where pushed.

Skills

Required

  • SQL
  • Python
  • Google BigQuery
  • Marketing Analytics
  • GTM Strategy
  • Data Science
  • Data Warehouse Architecture
  • Predictive Modeling
  • Causal Inference
  • LLM-powered agents
  • Orchestration patterns

Nice to have

  • Snowflake
  • Grafana
  • Claude Code
  • N8N

What the JD emphasized

  • build the systems that make every analyst on this team sharper, including the AI agents themselves
  • move beyond traditional reporting to build a self-sustaining marketing ecosystem
  • architect a dual-track target-setting framework
  • engineer the underlying data schemas, design predictive models, and implement the BI frameworks
  • prevent AI-generated noise from drowning out actual signals
  • Deploy LLM-powered agents
  • Implement orchestration patterns
  • Build and deploy AI-driven scoring models
  • Demonstrated history as a force multiplier. You can point to specific tooling, rituals, evaluator systems, or frameworks you built that made other analysts or the broader org measurably better. A portfolio of dashboards you personally produced is not sufficient.
  • Hands-on AI fluency as a build

Other signals

  • AI agents
  • predictive models
  • data stack evolution
  • marketing analytics