Senior Analytics Engineer, Jellyvision

• 7–9+ years in analytics engineering, data engineering, or BI, with deep hands-on ownership of the modeling and reporting layer
• Deep dbt expertise: project design (staging/intermediate/marts), incremental models, tests, macros, exposures, and documentation
• Strong dimensional and semantic modeling: dimensional design, grain decisions, metric definition, and building a reusable single source of truth
• Advanced SQL and fluency with a cloud warehouse (Snowflake or comparable): complex transformations and performance-aware modeling
• Strong BI/dashboarding skills in a modern tool (Omni, Looker, Power BI, Tableau, or similar), grounded in data modeling rather than one-off charts, including row-level security and performance
• A disciplined approach to analytics engineering: clear project structure and conventions, solid Git and CI/CD practices, and sound judgment about where logic belongs across the dbt and BI layers
• Ability to translate ambiguous business questions into modeled metrics and clear dashboards, and to communicate clearly with non-technical stakeholders
• Experience using AI-assisted development tools (Claude Code, Cursor, Copilot, or similar) as a genuine part of how you work
• A fast, iterative working style: you ship well-built work and refine it rather than perfecting in isolation, you're comfortable making progress amid ambiguity, and you're just as effective improving something you've inherited as building something new

Desirable

• As a Senior Analytics Engineer, you'll own the layer between our data platform and the business — the models, metrics, and dashboards that turn raw data into something people trust and act on.
• Working closely with our platform data engineers, who own ingestion, pipelines, and infrastructure, you'll pick up where they leave off: shaping warehouse data into well-modeled, well-tested datasets in dbt, and building the dashboards and reporting in our BI layer that leadership, product, and operations rely on every day.
• The modeling is the craft; the dashboards are the point — you'll be responsible for both, and for making sure the numbers people see are ones they can trust
• Design and build data models in dbt — staging, intermediate, and mart layers — with sound structure, incremental logic, tests, and documentation
• Apply dimensional modeling and grain discipline (star schemas, slowly-changing dimensions) so models are correct, performant, and reusable
• Define core business metrics once, correctly, and in a way the whole organization can rely on
• Classify sensitive data, maintain lineage and documentation, and define data-quality expectations at the model layer, partnering with the platform team on enforcement
• Success looks like: Models are trusted, well-tested, and reused rather than rebuilt. Metric definitions are consistent everywhere they appear
• Build and maintain the semantic layer between marts and dashboards, so a given metric means the same thing across every report
• Partner with the business to define metrics and reconcile competing definitions into a single source of truth
• Keep modeling documentation clear enough that anyone can understand what a metric means and how it's derived
• Success looks like: The business argues about decisions, not about which number is right
• Build dashboards and reporting in our BI stack (Omni), grounded in well-modeled data rather than one-off queries
• Handle row-level security, performance, and access so dashboards are reliable and appropriate for a regulated, multi-tenant environment
• Translate ambiguous business questions into modeled metrics and clear, usable dashboards, and communicate data caveats plainly
• When a dashboard looks off or a number doesn't reconcile, you can trace the issue back through the BI layer, the models, and the data to find the root cause and fix it
• Success looks like: People use the dashboards you build to make real decisions, and trust the numbers when they do
• Establish clear conventions for how analytics work is structured, tested, and deployed — a coherent project layout, sound CI/CD, and a clear rationale for what's modeled in dbt versus the BI layer
• Review analytics work across the team to keep models durable, tested, and consistent with our standards
• Set and evolve modeling and BI patterns that make everyone's work better and faster, and share knowledge through code review, pairing, and documentation
• Success looks like: Analytics work is consistent and easy to follow, quality holds even as we move quickly, and the team's models and dashboards get better over time