Product Owner managing product backlog and agile execution for Capgemini's digital transformation projects. Leading collaboration across teams and mentoring junior Product Owners to ensure alignment with business goals.
About the role
- Product Manager specializing in data and analytics capabilities at TomTom. Collaborating with cross-functional teams to shape data-driven product strategies in Amsterdam.
Responsibilities
- Own the vision, strategy, and roadmap for data & analytics capabilities that enable product teams to be truly data-driven and product-led
- Lead the end-to-end product lifecycle—from discovery and scoping through development, rollout, and continuous improvement
- Drive cross-team alignment and stakeholder buy-in across engineering, data science, and business teams
- Advance trusted metrics and governance: shape frameworks for metric definitions, quality, monitoring, and alerting
- Evolve our data architecture—helping move toward data mesh-inspired models (decentralized ownership, federated governance) while balancing centralized needs
- Contribute to key technical decisions—you’re fluent discussing data storage, APIs, event schemas, and analytics/ML needs with engineers and data scientists
- Champion operational excellence—embed data quality practices, efficient pipelines, access controls, and compliance guardrails into daily work
- Evangelize and educate: help build a data-driven culture through playbooks, enablement, and training
Requirements
- 5+ years in Product Management (or similar roles building data platforms/products), delivering outcomes with cross-functional teams
- Technical depth: comfortable with data architectures and analytics workloads; able to discuss schemas, telemetry, storage options (warehouses/lakes), APIs, and experimentation frameworks
- Experience with data mesh or similar decentralized data ownership/governance models, or migrating from silos to hybrid approaches
- Proven ability to set vision/OKRs, make clear product decisions amid ambiguity, and align multiple stakeholder groups
- Track record improving metrics quality and trustworthiness (definitions, monitoring, anomaly detection, freshness/latency) and enabling self-service analytics at scale
- Exposure to AI/ML enablement for product teams (agent-assisted analytics, analytics stores optimized for LLMs, vector search, etc.)
- Experience shaping data governance & access control across multiple systems with varying sensitivity levels
Job title
Job type
Experience level
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Not specified
Degree requirement
Location requirements
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