Staff Data Engineer

Moxie is hiring a Staff Data Engineer to own its data infrastructure, including pipelines, the Snowflake warehouse, and the modeling layer. The role partners with Engineering and Finance to maintain reliable, business-critical data and build the foundation for Practice Intelligence and Practice 360.

Responsibilities:

• Day to day, you'll partner with Engineering on production schema changes (Postgres to Snowflake), work with Finance to model accounting logic, build and maintain pipelines, and keep business-critical data available for internal teams and the practices we serve
• Make every number Moxie publishes defensible: within six months the data stack is owned, alerted, lineage-mapped, and documented end to end, and "is the number right?" has a yes-answer for every priority metric flowing into Practice 360 decks, MBRs, Practice Intelligence reports, and Moxie Suite embedded reports
• Own the dbt and orchestration layers, maintaining and evolving Moxie's dbt models (including financial reporting logic such as cash vs. accrual basis and incentive calculations and their downstream effect on metrics like AOV) and taking over the Prefect and Snowflake Openflow orchestration layer along with the observability platform that monitors it
• Deliver one high-impact project in your first quarter by scoping and shipping either the accrual basis accounting data model (so dbt and Omni support both cash and accrual reporting as we move upmarket) or modelling historical EMR data to work with existing practice KPIs so owners can understand the performance of their business before/after joining Moxie; neither exists today
• Turn ad-hoc work into owned process by establishing the SOPs and KPIs the stack has been missing: a monthly Vanta security review, a scoping and execution process for production schema changes, a triage workflow from job and test failures to resolution, and monthly infrastructure KPIs for performance and cost
• Deepen the moat: by month nine you're proactively optimizing the stack (for example, Omni data refreshed every 15 minutes at predictable cost) and building the pipelines and models that move Practice Intelligence from descriptive to predictive to prescriptive, including safeguards for LLM-generated content in data products

Requirements:

• 5-10 years of data engineering experience, with at least one stretch as an early data hire who took a system from "basics" to business-critical
• Deep dbt and SQL modeling skill applied to business logic: you've owned a dbt project of real complexity and can model financial concepts like cash vs. accrual accounting, incentive logic, and revenue definitions, and explain how a change upstream ripples through every downstream metric
• Python and software engineering fundamentals strong enough to own orchestration: you've run production pipelines on Prefect, Airflow, Dagster, or similar, handled schema evolution from an operational database into a warehouse, and set up alerting and observability so failures are caught before stakeholders notice
• A track record of leading end-to-end delivery: you gather requirements from non-technical partners (Finance, Product, Sales), scope the work, make the architectural call, ship it, and can articulate the tradeoffs you chose and the business value that resulted
• An ownership mindset about uptime, reliability, and cost; you treat data infrastructure as a living system that needs a steward, and SLAs, runbooks, KPIs, and cost optimization are things you build rather than wait to be asked for
• 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
• Someone who sees a data team as a driver of business outcomes, not a ticketing service; if "taking tickets from a queue" describes your ideal job, this won't be a fit