VP of Data
VPs of Data own all data assets, infrastructure, and teams. They lead centralized data organizations.
Median Salary
$340,000
Job Growth
High — data functions consolidating under VPs
Experience Level
Entry to Leadership
Salary Progression
| Experience Level | Annual Salary |
|---|---|
| Entry Level | $220,000 |
| Mid-Level (5-8 years) | $340,000 |
| Senior (8-12 years) | $480,000 |
| Leadership / Principal | $680,000+ |
What Does a VP of Data Do?
VPs of Data lead consolidated data organizations owning all data assets, infrastructure, and teams. They define data strategy, build and structure data organization, establish governance and quality standards, partner with business on data needs, and drive adoption of data across company.
A Typical Day
Strategy: Define data strategy covering engineering, science, analytics
Organization: Design org structure for 100+ person data function
Hiring: Recruit senior directors and team leads
Governance: Establish data governance, quality, security standards
Stakeholders: Partner with CFO, CTO, business leaders on data strategy
Roadmap: Prioritize infrastructure investments across teams
Culture: Build cohesive data culture across diverse teams
Key Skills
Career Progression
VPs of Data typically progress to Chief Data Officer or Chief Technology Officer roles.
How to Get Started
Deep experience: 10+ years in data (engineering, science, or analytics)
Leadership: Managed large teams and org structures
Technical breadth: Understand all data functions deeply
Strategy: Ability to think strategically about data
Executive: Executive presence and communication skills
Data-driven: Worked at data-centric organization
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Frequently Asked Questions
What does VP of Data own?▼
All data teams (engineering, science, analytics), infrastructure, governance, strategy. Consolidates what were separate functions.
Why is this role growing?▼
Companies realize data is competitive advantage. Need centralized strategy and governance. Benefits of unified approach.
What size team?▼
Depends on company. Can be 30-300 person organization managing all data functions.
What's the challenge?▼
Org design (how to structure teams), managing different cultures (engineers vs. analysts vs. scientists), prioritization across many demands.
What's success?▼
Data infrastructure that scales, high-quality data, analytics enabling decisions, ML models delivering value.
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Last updated: 2026-03-07