Marketing Analytics Director | Canada | Remote

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

This role is for a Marketing Analytics Director who will lead the evolution of the marketing data stack and raise analytical rigor. The role involves building systems that leverage AI agents and Google BigQuery to create a unified source of truth and a culture of analytical rigor. Key responsibilities include architecting a dual-track target-setting framework, engineering data schemas, designing predictive models, and implementing BI frameworks. The role also involves deploying LLM-powered agents for monitoring datasets, implementing autonomous workflows for data pipelines, and building 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. Move beyond descriptive analytics to perform causal inference and predictive trend analysis. When the data shows an anomaly, whether a 3-month spike, a regional dip, or a campaign that overperformed, isolate the window and dig in.

Skills

Required

  • SQL
  • Python
  • Google BigQuery
  • Snowflake
  • machine learning models
  • LLM-powered agents
  • orchestration patterns

Nice to have

  • Claude Code
  • MCP-based tooling
  • 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
  • engineer the underlying data schemas
  • 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
  • LLM-powered agents
  • predictive models
  • data warehouse architecture
  • causal inference