Senior Analytics Engineer, Gtm

ClickHouse ClickHouse · Data AI · United States · Finance

This role focuses on building and maintaining data models and analytical workflows for a Go-To-Market (GTM) organization, with a specific emphasis on enabling AI-driven insights and natural language access to data. While the role leverages AI tools and aims to create AI-ready data models, its core function is in analytics engineering and supporting GTM operations, not in building core AI models or systems.

What you'd actually do

  1. Own and develop core data models, datasets, and dashboards that serve as the foundation for GTM reporting and decision-making
  2. Enable a single source of truth for GTM metrics by partnering with cross-functional stakeholders to define, standardize, and operationalize key business logic
  3. Deliver insights and recommendations that improve GTM performance and inform strategic decisions
  4. Build scalable data systems and workflows that improve data accessibility, reliability, and usability across the organization
  5. Develop AI-ready data models and self-service solutions that enable natural language access and automated insights

Skills

Required

  • 5+ years of experience in analytics, BI, or data roles within a SaaS or technology company
  • Experience supporting GTM, sales, or revenue operations functions and working directly with Salesforce and related GTM systems
  • Strong experience supporting GTM, sales, or revenue operations functions
  • Highly proficient in SQL
  • experienced in building analytical data models with dbt or similar tools
  • Has experience working with modern data warehouses such as ClickHouse, Snowflake, or BigQuery
  • Proficient in Python for data analysis, automation, or workflow development
  • Highly fluent with AI tools and workflows, including LLMs, AI coding assistants, and automation frameworks, and applies them effectively in analytical work
  • Communicates clearly and can translate complex data into actionable business insights
  • Comfortable operating in ambiguity and taking ownership of open-ended problems

What the JD emphasized

  • AI-ready data models
  • natural language access
  • automated insights
  • Highly fluent with AI tools and workflows