Marketing Analytics Director | United States | Remote

Grafana Labs

1d•$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 who operates 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. As the lead architect of the data-driven growth engine, the Director will connect high-level strategy with advanced technical execution, moving beyond traditional reporting to build a self-sustaining marketing ecosystem. This will involve leveraging Google BigQuery, Grafana, and agentic AI to establish a unified source of truth and foster a culture of analytical rigor. The primary goal is to impact demand generation across the entire marketing funnel and elevate the standards for analytical work production, challenge, and delivery within the organization. A key responsibility will be architecting a dual-track target-setting framework to balance high-velocity top-of-funnel growth with deep-funnel quality, ensuring GTM resources are optimized for user cohorts that drive Grafana adoption and long-term retention. The role requires engineering 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. 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.
Mastery of SQL and Python required.
Deep experience architecting data environments in Google BigQuery, Snowflake, or similar warehouses.
Hands-on AI fluency as a builder, not a user. You have built and shipped agentic systems with Claude Code, MCP, or comparable tools. You understand where LLMs fail and how to design around those limitations. You have built 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 up the chain.

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. 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.
Transform technical data outputs into clear, compelling narratives for the executive team and board. Deliver a succinct read and go deep where pushed.
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.