About the RoleWe're looking for a Senior Analytics Engineer to own Moxie's Semantic Layer - the data foundation that powers our most strategic business initiatives, from Practice Intelligence to the AI-driven insights embedded within the Moxie Suite product. You'll also be stepping into a high-visibility role where your data patterns, conventions, and analytical philosophy will directly shape how an ambitious medspa platform understands itself and its customers.
You'll be the second full-time engineer on a small, focused Data and Analytics team, partnering closely with a dedicated BizOps counterpart so you can go deep on the engineering side without getting pulled into analyst work. The team is moving fast - from descriptive reporting today toward predictive and prescriptive analytics by year one - and this role sits right at the center of that journey.
What You'll Do- Own and evolve Moxie's Semantic Layer (dbt + Omni), build benchmarking and segmentation datasets that surface actionable insights across key medspa performance pillars - patient acquisition, retention, operational performance, and more.
- Enable embedded AI within Moxie Suite by developing high-quality datasets that power conversational analytics and accurate recommendations, targeting 98%+ accuracy for core medspa operational metrics.
- Drive data trust across the business by leading the Revenue Reconciliation & Operations workstream within the Data Governance initiative - unblocking enterprise-critical reports like staff and service-level pay.
- Partner with BizOps to establish correlation and affinity metrics (e.g., 'Service A is highly correlated with increased retention') that enable the Sales and PSM teams to close more enterprise medspas.
- Lay the groundwork for predictive analytics by exploring methods that take Practice Intelligence from 'here's what happened' to 'here's what's going to happen - and here's what to do about it.'
We're Looking For- A strong data modeler with deep dbt and SQL expertise who can build clean, scalable, well-documented models that others trust - and who gets frustrated by numbers that can't be defended.
- Someone who bridges engineering and business: you're as comfortable discussing medspa KPIs like AOV and retention rate as you are optimizing SQL models or writing Python when the job requires it.
- 3-6 years of experience in analytics engineering or a closely related role, ideally with exposure to a vertical SaaS or healthcare-adjacent data environment where data quality really matters.
- An AI-curious builder - you mention AI tools in your workflow, you've experimented with LLMs, or you've come from a company known for bold AI adoption. Familiarity with tools like Cursor, Claude Code, or similar is a plus.
- A clear communicator who can translate data findings into business recommendations - not just deliver a dataset, but explain what it means and why it matters to non-technical stakeholders.
- Someone who has been an early or foundational data hire before, or is genuinely ready to step into that level of ownership for the first time.
Why Join Us- You help set the standard, not inherit one. As the second full-time engineer on the team, your modeling patterns and analytical philosophy become Moxie's - you're building the foundation, not maintaining someone else's.
- Data architecture competitors can't match. Moxie's vertical model captures distinct clinical events tied to providers and patients at a level of detail that competitor EMRs simply don't have. Every new service Moxie adds makes the dataset richer and more defensible.
- A true partner, not a ticket queue. You'll work alongside a dedicated BizOps Manager - they own the insights, you own the trust. That split lets each of you go deep instead of being half-analyst, half-engineer.
- Descriptive today, prescriptive by year one. The 9-month milestone is explicit: move Practice Intelligence from 'here's what happened' to 'here's what's going to happen, and here's what to do about it.' This is work that goes well beyond metric definitions.
- A modern stack with room to shape it. Snowflake, dbt, Omni, Airbyte, Prefect, GitHub - plus AI tooling like Claude Code, Cursor, and n8n. You'll actively influence how the stack grows, not just work within it.
