Product Manager – Data and Analytics

2 weken geleden

Amsterdam, Noord-Holland, Nederland TomTom Voltijd € 80.000 - € 120.000 per jaar

We're looking for a Product Manager to own the strategy, roadmap, and adoption of internal data and analytics capabilities used by product teams. You'll work closely with engineering, data science, and business stakeholders to translate real product needs into scalable platforms and best practices. 

What you'll do
  • 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
What you'll need
  • 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

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