Marketing Analytics Director | United States | Remote

Grafana Labs

13h•$178,503 - $214,203•Remote

About The Position

Grafana Labs is seeking a Marketing Analytics Director to lead the evolution of their marketing data stack and enhance the analytical rigor of the marketing organization. This role is crucial for a builder-practitioner at the intersection of Data Science, Marketing Strategy, GTM, and AI Operations. The individual will not only report on the funnel but also build systems to improve the capabilities of analysts and AI agents. The Marketing Analytics Director will be the lead architect of the data-driven growth engine, connecting high-level strategy with advanced technical execution. The goal is to move beyond traditional reporting to build a self-sustaining marketing ecosystem using Google BigQuery, Grafana, and agentic AI to establish a unified source of truth and a culture of analytical rigor. The role aims to impact demand generation across the marketing funnel and elevate the standards for analytical work production, challenge, and delivery within the organization. A dual-track target-setting framework will be architected to balance high-velocity top-of-funnel growth with deep-funnel quality, ensuring GTM resources are directed towards high-converting user cohorts optimized for Grafana adoption and long-term retention. The position requires engineering underlying data schemas, designing predictive models, and implementing BI frameworks to shape global GTM strategy. The ideal candidate is a technical expert capable of translating complex data into clear executive-level direction and proactively building systems to prevent AI-generated noise from obscuring actual signals.

Requirements

8+ years in Marketing Analytics, GTM Strategy, or Data Science, with at least 2 years in a lead architect capacity (IC track or player-coach; people management not required) within a high-growth SaaS or PLG environment.
Demonstrated history as a force multiplier, with specific tooling, rituals, evaluator systems, or frameworks built that measurably improved other analysts or the broader organization.
Mastery of SQL and Python.
Deep experience architecting data environments in Google BigQuery, Snowflake, or similar warehouses.
Hands-on AI fluency as a builder, including experience building and shipping agentic systems with Claude Code, MCP, or comparable tools.
Understanding of LLM limitations and experience designing around them.
Experience building evaluator agents, prompt-grading systems, or analytical quality tooling in production.
Hands-on experience building complex logic and integrations using N8N, custom API orchestration, or MCP-based tooling.
Advanced proficiency in modern data stack visualization tools (Grafana, Looker, Tableau) to build executive-grade dashboards.
Proven ability to create structure in highly ambiguous environments and build target-setting frameworks from scratch.
Deep familiarity with connectivity between Salesforce, marketing automation, and product-led data streams.
Executive presence with experience presenting to executive and board audiences, including sound judgment about what data is and isn't ready to share.

Nice To Haves

Bachelor's or Master's degree in a quantitative field (Data Science, CS, Statistics, Business Analytics).
MBA or MS in Data Science is a significant plus.

Responsibilities

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.
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.
Develop and maintain machine learning models (Attribution, MMM, LTV) to predict campaign impact and steer budget allocation toward the highest-ROI channels.
Oversee the structure of marketing data within Google BigQuery, ensuring a scalable single source of truth that connects product usage data with marketing touchpoints.
Move beyond descriptive analytics to perform causal inference and predictive trend analysis. Isolate anomalies in data (e.g., spikes, dips, overperforming campaigns) and investigate their causes.
Transform technical data outputs into clear, compelling narratives for the executive team and board, delivering succinct summaries and providing deeper insights when required.
Deploy LLM-powered agents (Claude Code, MCP-based tooling, or comparable) to monitor BigQuery datasets and automatically flag quality shifts in the funnel before they impact revenue.
Implement orchestration patterns (N8N, custom MCP servers, or equivalent) to build self-healing data pipelines and automated responses to market signals, such as automated spend shifts based on conversion anomalies.
Build and deploy AI-driven scoring models that separate high-value potential users from low-signal volume, helping Sales and Marketing prioritize effectively.