VP, Data & Analytics

LifeMD•New York, NY

9h•Hybrid

About The Position

LifeMD is looking for a VP of Data & Analytics to own our data platform end to end and lead the analytics engineering team that powers decisions across finance, marketing, and operations. This is a senior technical leadership role for someone who is equally comfortable shaping a dbt architecture, partnering with our Chief Accounting Officer on revenue recognition, and presenting insights to the executive team. You will sit at the intersection of engineering precision and business strategy. The data our team produces feeds earnings support, board materials, financial forecasts, and operational dashboards — accuracy and reliability are non-negotiable. At the same time, you will drive how we use AI to surface insights faster and build internal tooling that amplifies the team’s output.

Requirements

SQL & dbt Architecture. You write and review SQL at a senior level and have built or materially evolved a data platform on dbt and a cloud warehouse. You make architectural decisions about modeling, incrementality, testing, and governance.
GCP Experience. You have worked on GCP in production, including BigQuery and surrounding services such as GCS, Cloud Run, and Cloud Scheduler.
Finance & Revenue Recognition. You have owned or partnered closely on revenue recognition at a public company, and have contributed to financial modeling such as cohort forecasting, LTV, and unit economics. You can architect auditable data models and represent the data function credibly to accounting, auditors, and finance leadership.
Python Fluency. You are comfortable in Python and can read and modify production code. You can maintain and extend data systems beyond the warehouse, including API integrations, scheduled jobs, and cloud infrastructure.
AI Fluency. You have built internal tooling that uses LLMs. You use Claude Code, Cursor, or another AI-driven IDE for development and actively shape how AI gets woven into your team’s workflows.
Data Engineering Leadership. You have managed small data or analytics engineering teams where the work sits closer to engineering than to BI, and where reliability matters because many stakeholders depend on the output. You hire, develop, and retain technical talent and coach individual contributors.

Responsibilities

Own the full analytics stack: Fivetran and custom Python ingestions, dbt for transformation, BigQuery as the warehouse, and Hex and Posit Connect for reporting and analytics applications. The stack runs on GCP. You are accountable for its reliability, cost, governance, and evolution.
Manage a small team of analytics engineers and analysts organized by business function across finance, marketing, and operations. The work is analytics-engineering-forward — producing data that many downstream stakeholders depend on. You will hire, develop, and shape the team as the function matures.
Accountable for the accuracy, auditability, and architecture of revenue recognition processes, partnering closely with the Chief Accounting Officer and representing the data function directly to external auditors.
Contribute directly to financial modeling — cohort-based subscription forecasting, LTV, and budget vs. actual analysis. Ensure FP&A always has clean, timely, trusted data to build on, and serve as a technical partner to the finance function.
Maintain our in-house AI analytics platform as a production service, develop new functionality using AI-driven tools, and actively find new ways to surface insights with AI as part of the analytical toolkit.
Work closely with our engineering teams to inform data architecture decisions that align with analytics and reporting objectives.
Work directly with the executive team to produce the data that supports strategy — KPI dashboards, board materials, earnings support, and ad-hoc analysis — and to make recommendations based on it.