Lead development of the dbt semantic layer and analytics products, own data modeling and metrics, ensure data quality and documentation, support embedded analytics and AI initiatives, and partner with engineering, product, and partners to make data trusted and accessible.
MainstayPosition: Senior Analytics EngineerPosition Type: Full Time (Primarily Remote)Salary: $140,000 - $155,000 DOEJoin our team at Mainstay - a division of Lemnis! Lemnis is a public charity dedicated to harnessing transformative change to expand learning for all. We are excited to announce a new opportunity for a Senior Analytics Engineer at Mainstay. We are committed to fostering a collaborative and inclusive work environment where every team member can thrive. If you are passionate about expanding learning for all, eager to make a meaningful impact, and ready to take on new challenges, we would love to hear from you. Apply now and be a part of our journey!Position SummaryAt Mainstay, we believe one conversation can spark a brighter future. Our Engagement Platform makes it easy for colleges and businesses to start and measure conversations that drive action at scale. From our rigorous research methods to our Behavioral Intelligence framework — everything we do is designed to help people take the next step toward achieving their goals.We're hiring a Senior Analytics Engineer to build the data foundation that powers Mainstay's analytics, AI initiatives, and the products our partners depend on. This is a high-leverage, foundation-building role. You'll own our semantic layer in dbt, lead the development of internal and partner-facing analytics products, and make our data trusted, accessible, and AI-ready. You'll work alongside our Senior Data Engineer - they own the infrastructure and pipelines; you own the modeling, metrics, and the products and documentation that make data usable across the company. If you care about designing tables that empower people to answer their own questions, catching data quality issues before users see them, clean naming conventions, and treating documentation as a product - this role is built for you.
Responsibilities
Own and expand our semantic layer
- 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
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