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Moxie is a B2B SaaS platform empowering aesthetic entrepreneurs to build profitable, independent practices. The company has grown to support 700+ practices nationwide with a global, remote-first team.
You will own Moxie's data infrastructure end-to-end: orchestration pipelines (Prefect, Airbyte, Snowflake Openflow), the data warehouse (Snowflake), and the modeling layer (dbt, Omni). Data and intelligence are core to Moxie's competitive advantage, and this role closes the gap between current capabilities and full potential.
Key responsibilities include:
- Partner with Engineering on production schema changes (Postgres to Snowflake) and with Finance to model accounting logic
- Build and maintain pipelines ensuring business-critical data availability for internal teams and customer practices
- Make every published number defensible: own the data stack end-to-end with alerting, lineage mapping, and documentation within six months
- Own the dbt and orchestration layers, maintaining financial reporting logic (cash vs. accrual basis, incentive calculations) and their downstream effects on metrics
- Deliver a high-impact project in Q1: either build an accrual basis accounting data model or model historical EMR data for practice KPIs
- Establish SOPs and KPIs for the stack: monthly security reviews, production schema change processes, job failure triage workflows, and infrastructure KPIs
- Proactively optimize the stack and build pipelines moving Practice Intelligence from descriptive to predictive to prescriptive analytics
This is a highly visible individual contributor role on a small, ambitious team. You'll set data patterns and conventions that become Moxie's standard, work with a modern stack (Snowflake, dbt, Omni, Airbyte, Prefect, GitHub), and have direct input on the data roadmap and your career trajectory.
REQUIREMENTS:
- 5–10 years of data engineering experience, including at least one stretch as an early data hire who scaled a system from basics to business-critical
- Deep dbt and SQL modeling expertise applied to business logic: owned a complex dbt project, modeled financial concepts (cash vs. accrual accounting, incentive logic, revenue definitions), and can trace upstream changes through downstream metrics
- Strong Python and software engineering fundamentals sufficient to own orchestration: production pipeline experience with Prefect, Airflow, Dagster, or similar; schema evolution from operational databases to warehouse; alerting and observability setup
- Track record of leading end-to-end delivery: gathering requirements from non-technical partners (Finance, Product, Sales), scoping work, making architectural decisions, shipping, and articulating tradeoffs and business value
- Ownership mindset about uptime, reliability, and cost; treat data infrastructure as a living system requiring stewardship; build SLAs, runbooks, KPIs, and cost optimization proactively
- Fluency with AI tooling in daily work (Claude Code, Cursor, n8n, or similar) and a point of view on where LLMs belong in a data stack and where they need guardrails
- View data teams as drivers of business outcomes, not ticketing services
