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Salary: USD 146,000 - 230,000 / annual
Cloudflare is seeking a Senior GTM Analytics Manager to lead the vision and execution of analytics scaling across the company's technical customer-facing teams, including Pre- and Post-Sales Operations, Solutions Engineering, Forward Deployed Engineering, and Professional Services.
This is a senior individual contributor role with wide influence and no direct reports. The focus is less on building dashboards or one-off analyses and more on establishing foundations for true self-serve analytics. You will own single source of truth artifacts, align metrics to business actions, and design data models and metric definitions that enable anyone—executives, frontline managers, field engineers, or AI agents—to get trustworthy answers.
Key responsibilities include:
- Define and drive a multi-year strategy for how Pre-Sales, Post-Sales, Solutions Engineering, FDE, and Professional Services find, trust, and use data, including a maturity model and phased roadmap.
- Act as a thought leader on scaling information in an AI-first world, working out how semantic layers, governed metrics, and well-built context enable AI assistants and agents to give accurate, consistent answers.
- Review the current mix of dashboards, reports, and tools across these teams; create a plan to consolidate, retire duplicates, and move toward a single source of truth.
- Define, document, and carry out official metrics for supported teams; keep definitions aligned with corporate Finance and Data definitions, and resolve conflicts with clear logic.
- Act as the main business and technical partner to the central Data team; influence and review data model design across Salesforce, professional services tools, product usage and telemetry, support, and finance systems.
- Design how people get answers: self-serve access, natural-language and AI-assisted querying, embedded insights in daily workflows, and curated metric catalogs.
- Set up a formal intake and governance process for new metric and data requests, including prioritization, ownership, certification, and change management.
- Build trusted relationships with leaders across Sales, Solutions Engineering, FDE, Professional Services, Customer Success, Operations, Finance, and Data.
- Explain complex technical and analytical ideas clearly to executives to support decision-making.
You will be a technical expert and strategic partner, equally comfortable reviewing dimensional models with Data Engineering and agreeing metric definitions with a VP of Professional Services. You'll be the go-to voice on how analytics should evolve in an AI-first world.
Requirements:
- 8+ years in analytics, business intelligence, analytics engineering, or GTM/Services operations at a high-growth B2B SaaS or technology company.
- Direct experience supporting at least one of: Pre- or Post-Sales Operations, Solutions/Sales Engineering, Forward Deployed Engineering, Professional Services, or Customer Success. Must understand how these teams work, how they're measured, and how their data is created.
- Deep data modeling expertise, including dimensional modeling, entity design, and metric layer design. Must be able to review and challenge a data model, not just consume one.
- Expert SQL skills and hands-on experience with modern data stack tools such as BigQuery, dbt, or a semantic/metrics layer (e.g., dbt Semantic Layer, LookML, or Cube).
- Strong working knowledge of GTM and services systems data, such as Salesforce, professional services automation tools, and product usage or telemetry data.
- Clear, informed view on how AI changes analytics, including semantic layers, retrieval and context design for LLMs, text-to-SQL, and agent-based access to governed data, plus the data foundations those require to be accurate.
- Track record of defining and rolling out source-of-truth metrics across multiple stakeholder groups, including handling disagreements over definitions.
- Proven ability to lead complex, cross-functional programs without formal authority, and to influence executives and engineers alike.
- Excellent written and verbal communication skills, including turning ambiguous business questions into precise technical requirements and back again.
- BA/BS in a quantitative, technical, or business field. Master's in Data Science, Analytics, Computer Science, or MBA is a plus.
