We're looking for our very fist Staff Analytics Engineer to help scale the foundation that powers decision-making across Product, Engineering, Finance, Revenue, and Executive leadership. This is a highly technical individual contributor role that combines data modeling, architecture, analytics engineering, and cross-functional leadership. You'll work at the intersection of business context and data platform strategy, designing trusted datasets, defining best practices, and helping shape the future of analytics at MNTN.

What you'll do

• Lead the design and evolution of scalable, trusted dimensional models including well-defined facts and conformed dimensions that power reporting, product analytics, forecasting, and decision-making across the business.
• Treat analytics code as software: establish standards for modular, DRY transformations, automated testing, version control, CI/CD, documentation, and governance so data quality and consistency are enforced by the system rather than by manual effort.
• Replace brittle, high-maintenance pipelines with reusable, automated patterns (incremental models, declarative transformations, reusable macros) that reduce toil and keep the platform maintainable at scale.
• Partner with engineering to shape data architecture, improve platform performance, and support long-term scalability.
• Collaborate with stakeholders across Product, Finance, Revenue, and Operations to translate business needs into durable analytical solutions.
• Drive adoption of semantic layers, shared metrics, and self-service analytics capabilities that increase trust and accessibility across teams.
• Provide technical leadership through architectural guidance, mentorship, and influence across the broader data organization.

What success looks like

• Trusted, well-documented dimensional models and metric definitions exist across key business domains, reducing reporting inconsistencies and increasing confidence in decision-making.
• Analytics engineering standards for modeling, automated testing, governance, and documentation are established and broadly adopted across the data organization.
• The pipeline runs reliably with minimal manual intervention; new data needs are met by extending well-structured models rather than building yet another bespoke table.
• Stakeholders across Product, Finance, Revenue, and Operations can access reliable self-service data resources without requiring constant support from technical teams.
• The analytics platform is more scalable, performant, and maintainable because of systems, processes, and architectural improvements you've led.

What you'll bring

• 8+ years of experience in analytics engineering, business intelligence, data engineering, or a related data discipline.
• Deep, hands-on expertise in Kimball-style dimensional modeling including fact and dimension table design, grain definition, surrogate keys, slowly changing dimensions, conformed dimensions, and star (vs. snowflake) schema tradeoffs plus the judgment to know when to apply or break these patterns.
• Expert SQL and strong command of dbt or SQLMesh, with a demonstrated habit of building modular, tested, version-controlled transformations rather than manually maintained scripts.
• Production experience with modern cloud data warehouses such as Snowflake, Databricks, BigQuery, or Redshift.
• A track record of building automated, low-toil analytical systems including incremental processing, CI/CD, data tests/contracts, that stay maintainable as data and team size grow.
• Proven ability to influence cross-functional stakeholders and drive alignment on metrics, reporting, and data strategy.
• Experience leading large-scale analytics initiatives with company-wide impact, operating as a senior technical leader without direct authority.
• Strong communication skills with the ability to translate complex technical concepts into clear business outcomes.

Bonus points if you have

• Experience supporting product analytics, experimentation, or marketplace platforms.
• Familiarity with modern BI and semantic layer tools such as Looker, Sigma, Hex, or Tableau.
• Experience in adtech, martech, SaaS, or other high-scale data environments.
• Exposure to machine learning, AI applications, or advanced analytics workflows.
• Experience helping organizations mature data governance and metric standardization practices.