VP, Data & Analytics

LifeMD•New York, NY

1d•Hybrid

About The Position

LifeMD is seeking a VP of Data & Analytics to oversee their data platform from end to end and lead the analytics engineering team. This role is crucial for supporting decisions across finance, marketing, and operations. The ideal candidate will be comfortable with dbt architecture, collaborate with the Chief Accounting Officer on revenue recognition, and present insights to the executive team. This position sits at the intersection of engineering precision and business strategy, producing data for earnings support, board materials, financial forecasts, and operational dashboards. The role also involves driving AI integration for faster insights and building internal tooling to enhance team output.

Requirements

Experience owning 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.
Experience ensuring data quality, lineage, and standardization.
Experience collaborating with Finance, IT, and Security teams on governance and control frameworks.
Experience managing analytics engineers and analysts.
Experience hiring, developing, and retaining technical talent.
Experience with financial modeling, including cohort-based subscription forecasting, LTV, and budget vs. actual analysis.
Experience maintaining an in-house AI analytics platform.
Experience developing new functionality using AI-driven tools.
Experience working closely with engineering teams on data architecture.
Experience working directly with executive teams on strategic data initiatives.
Senior-level SQL proficiency.
Experience building or materially evolving a data platform on dbt and a cloud warehouse.
Experience making architectural decisions about modeling, incrementality, testing, and governance.
Experience working on GCP in production, including BigQuery and surrounding services (GCS, Cloud Run, Cloud Scheduler).
Experience owning or partnering closely on revenue recognition at a public company.
Experience contributing to financial modeling such as cohort forecasting, LTV, and unit economics.
Ability to architect auditable data models.
Ability to represent the data function credibly to accounting, auditors, and finance leadership.
Python fluency.
Comfort reading and modifying production Python code.
Experience maintaining and extending data systems beyond the warehouse.
Experience building internal tooling that uses LLMs.
Experience using AI-driven IDEs like Claude Code or Cursor.
Experience shaping how AI is woven into team workflows.
Experience managing small data or analytics engineering teams where reliability is critical.
Experience coaching individual contributors.

Responsibilities

Own the full analytics stack including Fivetran and custom Python ingestions, dbt for transformation, BigQuery as the warehouse, and Hex and Posit Connect for reporting and analytics applications.
Ensure the reliability, cost, governance, and evolution of the data platform running on GCP.
Guarantee data quality, lineage, and standardization throughout the data ecosystem.
Collaborate with Finance, IT, and Security teams on the overall governance and control framework.
Manage a team of analytics engineers and analysts organized by business function (finance, marketing, operations).
Hire, develop, and shape the analytics engineering and analyst team as the function matures.
Oversee the accuracy, auditability, and architecture of revenue recognition processes, partnering with the Chief Accounting Officer.
Represent the data function directly to external auditors.
Contribute directly to financial modeling, including cohort-based subscription forecasting, LTV, and budget vs. actual analysis.
Ensure FP&A has clean, timely, and trusted data for financial modeling.
Serve as a technical partner to the finance function.
Maintain the in-house AI analytics platform as a production service.
Develop new functionality using AI-driven tools.
Actively find new ways to surface insights with AI as part of the analytical toolkit.
Work closely with engineering teams to inform data architecture decisions aligned with analytics and reporting objectives.
Work directly with the executive team to produce data supporting strategy (KPI dashboards, board materials, earnings support, ad-hoc analysis).
Make recommendations based on data insights to the executive team.
Write and review SQL at a senior level.
Build or materially evolve a data platform on dbt and a cloud warehouse.
Make architectural decisions regarding modeling, incrementality, testing, and governance.
Maintain and extend data systems beyond the warehouse, including API integrations, scheduled jobs, and cloud infrastructure.
Manage small data or analytics engineering teams where reliability is critical.
Hire, develop, and retain technical talent and coach individual contributors.