Senior Product Manager, Data Platform & AI

Wolters Kluwer International

Alphen aan den Rijn

Hybrid

EUR 120,000 - 160,000

Full time

4 days ago

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Job summary

Enablon, part of Wolters Kluwer, is shaping the data foundation behind analytics and AI across its product portfolio. As Senior Product Manager, Data Platform, you will set the direction for the data platform and evolve it into a reliable, governed base for analytics and AI-enabled experiences.

You will own the vision, strategy and roadmap, collaborate with data engineers, architects and business leaders, and partner with a vendor to deliver a scalable platform that enables trusted insights for

Qualifications

6+ years of product management in enterprise software, platform or data-intensive products.
Ownership of a data platform, data products or shared platform capabilities used by several teams, not only end-user features.
Experience defining strategy for data platforms, master or reference data, interoperability, APIs, analytics foundations or data governance, and turning it into a sequenced roadmap.
Credibility with architects, data engineers and software engineers on pipelines, storage, APIs and governance.
Experience working across many teams and stakeholders in a matrixed organisation, with executive-level communication skills.
Bachelor's or master's degree in a technical or related field.
Hands-on experience with lakehouse or cloud data platforms such as Databricks, Snowflake, Microsoft Fabric or similar; streaming and change-data-capture patterns; data foundations for AI products; experience in regulated enterprise domains.

Responsibilities

Own the strategy and roadmap for the data platform.
Decide which data the platform makes available, in what shape, for which consumers and in what order, and make each investment trade-off explicit and tied to a customer or business outcome.
Treat data as a product. Define the data products, semantic models and shared data services the portfolio relies on, and set the standards for quality, lineage, governance and access so that analytics and AI teams can trust what they consume.
Partner with data engineering and architecture on how the platform evolves: streaming from operational systems to the lakehouse, storage and compute, and the boundary between what the platform provides and what a product builds itself.
Run discovery with the platform's consumers, product teams, analytics, AI teams and customers; validate assumptions before large investments, measure adoption and value, and adjust.
Align the portfolio behind shared data priorities, explain platform value to non-technical executives, and manage the relationship with our data platform vendor.