VP Data: Salary and Missions in 2026

The VP Data (VP of Data) is the executive leader of the data function within the company. They steer the overall data strategy: data infrastructure, analytics, data science, machine learning, and data governance. They are typically found at scale-ups where data has become a strategic asset (B2B SaaS, marketplaces, fintech, e-commerce, AI-first).

Job profile last updated on 11/06/2026.

Why hire a VP Data?

When a company has 3+ data teams (data engineering, analytics, ML/AI), those teams need a leader who thinks of data as a company asset, not just a cross-functional support function. Without a VP Data, the data stack becomes incoherent (each team picks its own tools), ML models stay stuck in POC, and governance descends into chaos.

What is the role of the VP Data?

The VP Data reports to the CEO, CTO, or COO depending on the organisation. They manage the Head of Data Engineering, Head of Analytics, Head of ML/AI, and sometimes the Chief Data Officer (CDO) depending on the structure. They define the data strategy, choose platforms (Snowflake, Databricks, BigQuery), and ensure data creates genuine business value — not just dashboards.

They sponsor major data projects: platform modernisation, LLM upskilling, regulatory compliance (GDPR, AI Act), and data monetisation.

What are the missions of the VP Data?

• Define the data strategy: 2–3 year vision, platforms, target organisation.
• Manage data leads: recruitment, coaching, team structuring.
• Ensure quality and governance: data contracts, observability, lineage, access control, compliance.
• Align data with the business: ensure every data project has a measurable impact (revenue, cost, quality).
• Industrialise ML/AI: MLOps processes, model monitoring, stable production deployments.
• Represent data at the exec level: reporting, budget defence, AI awareness.

What are the key skills?

The VP Data combines solid technical expertise, strategic thinking, and the ability to manage very different profiles (data engineers vs ML scientists vs analytics). In particular:

• 10+ years of data experience with 3+ in management
• Mastery of modern platforms (Snowflake, Databricks, dbt, Airflow, Kafka)
• Experience deploying ML models at scale
• Strong business understanding: actionable analytics, data products
• Knowledge of governance and compliance (GDPR, AI Act, ISO)

Soft skills

Ability to translate data for the executive committee, to arbitrate between competing projects, to motivate data teams that are in high demand on the job market, and to manage data legacy (often substantial) without losing sight of it.

What is the salary of a VP Data?

A VP Data in France typically earns between 100K€ and 160K€ gross per year + a 15–25% variable + equity at scale-ups. In a well-funded AI-first company (Series B/C+ with central AI usage), salaries exceed 180K€ fixed.

How does a VP Data's career evolve?

The VP Data progresses towards Chief Data Officer (C-level equivalent, often in larger groups), CPO or CTO in some data-centric organisations, or starts their own venture (often a data/AI company). Others become Operating Partners for data/AI at a VC fund.

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FAQ about the VP Data

What is the difference between a VP Data and a Chief Data Officer (CDO)?

The VP Data is an operational executive role: they manage data teams, steer platforms, and ensure delivery. The CDO is a C-level strategic role that sets the company-wide data policy, often with a governance, compliance, and monetisation dimension. In some organisations the two roles overlap; in others, the VP Data reports to the CDO.

What is the salary of a VP Data in France in 2026?

A VP Data in France typically earns between 100K€ and 160K€ gross per year, plus a 15–25% variable and equity at scale-ups. In well-funded AI-first companies (Series B/C+), packages regularly exceed 180K€ fixed.

What technical skills are essential for a VP Data?

The VP Data must master modern platforms (Snowflake, Databricks, dbt, Airflow, Kafka), have concrete experience deploying ML models in production, and understand governance requirements (GDPR, AI Act). Knowledge of cloud architectures (AWS, GCP, Azure) and MLOps practices is expected.

From what company size should you hire a VP Data?

A VP Data becomes relevant when the organisation has at least 3 distinct data teams (data engineering, analytics, ML/AI). Below that, a Head of Data usually covers the scope. The clearest signal is data silos multiplying and the absence of a common vision on the platform and priorities.

How does the VP Data differ from the VP Engineering?

The VP Engineering manages the entire product engineering function (backend, frontend, infrastructure), while the VP Data focuses on the data layer: pipelines, analytical models, machine learning, and data governance. The two roles collaborate closely, particularly on infrastructure and APIs, but their areas of responsibility are distinct.

What are the main challenges a VP Data must tackle?

The most common challenges are data quality and reliability (data contracts, observability), stable ML model deployment (MLOps), aligning data projects with business priorities, regulatory compliance (GDPR, AI Act), and continuous upskilling of teams in the face of rapidly evolving AI.

How do you evaluate a VP Data during recruitment?

Good indicators are: a track record of data projects with measurable impact (revenue, cost, quality), experience in structuring and managing multidisciplinary teams, the ability to explain data to an executive committee, and a clear vision on platforms and data architecture. A practical case based on a real company challenge is often the best filter.

What career path typically leads to the VP Data role?

Most VP Datas are former Data Scientists, Data Engineers, or Heads of Analytics who moved into management. A stint as Head of Data or Director of Data is almost always a prerequisite. Some come from consulting or Data Architect roles at large companies. The common denominator is solid technical experience combined with the ability to manage teams and speak the business language.