Responsibilities

Lead semantic layer development in dbt as the single source of truth for metrics across dashboards, reports, AI tools, and embedded analytics
Partner with the Senior Data Engineer on data architecture and quality testing for data products
Deprecate legacy reporting methods and drive adoption of the semantic layer as the default
Translate technical concepts and internal terminology into business language so data is intuitive for non-technical users

Build analytics products that drive business value

Lead refreshes and new builds of internal and partner-facing analytics products that help us identify risks and surface opportunities
Support embedded analytics work that brings customer-facing dashboards directly into the product
Partner with Partner Success, Product, and Leadership to gather requirements and design analytics products that answer the question behind the question

Make data trusted, accessible, and easy to use

Own our internal data knowledge base - the centralized source for documentation, metric definitions, and lineage
Create documentation, short-form videos, and dashboard guides that help employees use data confidently
Support cross-functional data power users for knowledge sharing and feedback on data products
Partner on data literacy training and onboarding, including new-hire modules and annual refreshers

Support our AI initiatives

Build and maintain the verified query repository and curated data assets that power our internal AI agents
Monitor agent performance and identify gaps in context or data that can be addressed at the data layer

Qualifications

5+ years in analytics engineering, data analytics, or BI engineering, with at least 3 years owning data modeling end-to-end
Strong SQL and production experience with dbt (you've built, tested, and maintained models, not just tinkered)
Hands-on experience with a modern cloud warehouse (Snowflake, BigQuery, Databricks, or Redshift)
Experience with software engineering best practices (git, CI/CD, PRs)
Solid data modeling fundamentals - dimensional modeling, slowly-changing dimensions, OLTP vs OLAP, knowing when to materialize vs. view
Experience with a modern BI tool (Sigma, Looker, Hex, Tableau, Mode, or similar)
Excellent written communication - you can explain a metric to a VP and document it for a new hire in the same afternoon
Strong stakeholder management - you've worked directly with non-technical teams and helped them ask better questions
Comfort with ambiguity and a bias toward making things simpler

Desired Attributes

Direct experience with semantic layers (dbt Semantic Layer, Cube, LookML)
Snowflake experience, especially with semantic views and Cortex
Familiarity with AI evals, prompt evaluation, or working alongside ML/AI initiatives
EdTech, higher education, or B2B SaaS background
Experience building documentation systems or running data enablement programs
Python proficiency for modeling and analysis work