Senior Analytics Engineer

Join Pantheon, a leading webops platform, as a Senior Analytics Engineer. In this role, you will own the analytics modeling lifecycle for a business domain, develop and maintain the semantic layer that powers our BI platform, and partner directly with stakeholders across various departments. You will also build trustworthy self-serve data products and collaborate with the Data Platform team. Enjoy a flexible work environment, health and wellness benefits, and opportunities for professional development.

• Ownership of the analytics modeling lifecycle for a business domain, including building and maintaining dbt models and business marts on top of source data in Snowflake.
• Collaboration with stakeholders across Finance, Sales, Marketing, CS, and RevOps to translate ambiguous business questions into the right data models and prescribing or designing one when none exists.
• Definition, documentation, and enforcement of consistent metric definitions and segmentation across the organization, establishing whether each lives in dbt or the semantic layer.
• BI / semantic-layer modeling experience, ideally Looker (LookML) or Omni
• Cloud data warehouse experience, ideally Snowflake
• 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)
• Strong dimensional/data-modeling fundamentals (Kimball design, star schema)
• 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)
• 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)