Senior Analytics Engineer

Pantheon WebOps Platform powers the open web, running more than 300,000 sites in the cloud for customers including Google, Princeton, Salesloft and Doctors Without Borders. Every day, thousands of developers and marketers create, iterate, and scale WordPress and Drupal sites to reach billions of people globally. Pantheon’s multitenant, container-based platform enables organizations to manage all of their websites from a single dashboard. Organizations including Clorox and the United Nations drive results through accelerated development and real-time publishing using Pantheon’s collaborative workflows. 

The Analytics Engineering team owns how Pantheon's data becomes trusted, self-serve insight — from the data models built on our raw source tables to the semantic layer the whole company consumes. Sitting close to the business (under Finance), you'll own a domain end-to-end and act as a partner who turns business questions into the right data models.

• Own the analytics modeling lifecycle for a business domain (GTM / Lead-to-Customer or Post-Sales) — build and maintain dbt models and business marts on top of our source data in Snowflake.
• Develop and maintain the semantic layer that powers our BI platform.
• Partner directly with stakeholders across Finance, Sales, Marketing, CS, and RevOps to translate ambiguous business questions into the right model — prescribing or designing one when none exists.
• Define, document, and enforce consistent metric definitions and segmentation across the org (e.g., customer lifecycle stages, production lifecycle, ARR), establishing whether each lives in dbt or the semantic layer so metrics aren't defined twice.
• Build trustworthy self-serve data products so business partners can answer routine questions without help.
• Collaborate with the Data Platform team where raw data is handed off — specify and request new sources, validate data, and raise quality issues upstream.

• SaaS finance fluency — ARR, MRR, NRR (especially valuable given we sit under Finance)
• Depth in GTM/RevOps data (Salesforce) or post-sales/CS data (Zendesk), depending on the domain
• Familiarity with AI- or natural-language-driven analytics
• Exposure to orchestration (Airflow), reverse ETL, or working alongside data engineering
• CI/CD for analytics code (e.g., dbt tests running on PRs via GitHub Actions)
  
  

• 6–8 years of overall experience in analytics, data engineering, or a related field, including at least 4 years specifically in analytics engineering.
• Advanced SQL and hands-on experience with dbt or a comparable transformation framework (models, tests, documentation)
• Cloud data warehouse experience, ideally Snowflake
• BI / semantic-layer modeling experience, ideally Looker (LookML) or Omni
• Strong dimensional/data-modeling fundamentals (Kimball design, star schema)
• Proven ability to translate business questions into data models — strong business acumen and stakeholder communication
• Comfortable with git and a modern analytics development workflow (branch-based PRs and code review)