2 days ago

Senior Product Manager Data Platform

bol

Utrecht

2 days ago

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bol is seeking a Senior Product Manager for its Data Analytics Platform team in Utrecht. The role involves owning the full product scope of data access, visualisation, and agentic analytics, driving strategy for tools like Tableau and Looker Studio, and leading AI-powered analytics initiatives in a federated data organisation. Requires at least 3 years of product discovery and delivery experience, with a blend of qualitative and quantitative skills.

Core AIHybridFull-timeSeniorBigQueryDatahub

Senior Product Manager Data Platform

Apply

Core AIHybridFull-timeSeniorBigQuery

Salary

Not specified

Work Location

Utrecht, Utrecht, Netherlands, NL

Work Model

Hybrid

Experience Required

3 years

Employment Type

Full-time

Experience Level

Senior

Core Qualifications

Technical (Must-have)

BigQueryDatahubTableauLooker StudioLLM integrationmetrics layersdata pipelinesproduct discoveryproduct deliveryagentic analyticsconversational interfacesAI-driven insight generationautomationevalstest-and-learn

Soft Skills

influence without authorityoutcome-drivenbroad by naturecomfort with ambiguitystrong convictions held lightlyprioritisationcompelling visionqualitative discoveryquantitative deep dives

Key Responsibilities

•Own the full product scope of the Data Analytics Platform, from how bollers access and find data, to how they visualise and share it, to how AI-powered interfaces increasingly do the querying for them.
•Lead the strategy for bol’s visualisation toolchain (Tableau, Looker Studio) diagnosing today’s fragmentation, defining what good looks like, and building toward a landscape that’s coherent, trusted, and self-serve by design.
•Define and drive the product vision for agentic analytics capabilities, including conversational interfaces, AI-driven insight generation, and automation of high-frequency analytical workflows.
•Lead the discovery of use-case specific capabilities and build a repeatable playbook for what comes next by translating use-case needs into scalable platform capabilities.
•Translate the complexity of LLM integration, metrics layers, and data pipelines into a clear, phased roadmap that engineering, analytics, and business stakeholders can all rally behind.
•Work directly with Analytics Engineering, Data Engineering and Agentic Operations Platform teams to ensure the agentic layer sits on a trustworthy, governed data foundation, not quicksand that leads to hallucination at scale.
•Navigate a highly federated data organisation: align dozens of domain teams, analytics engineers, and senior stakeholders across commercial, operational, legal, and technical functions, without central control as your lever.
•Define what “good” looks like across the whole platform: evals such as task success rates, visualisation adoption, time-to-insight, analyst time reclaimed, and instrument the systems to actually measure it.
•Champion a test-and-learn approach: run controlled experiments, learn from field tests, and know when to scale and when to kill.
•Drive internal enablement: making sure that when you build something, teams actually use it, trust it, and tell you what to build next.

Product ManagementData PlatformAnalyticsAIRetailSeniorHybridUtrechtNetherlandsAgentic Analytics

Core Qualifications

Technical (Must-have)

Soft Skills

influence without authorityoutcome-drivenbroad by naturecomfort with ambiguitystrong convictions held lightlyprioritisationcompelling visionqualitative discoveryquantitative deep dives

Key Responsibilities

•Own the full product scope of the Data Analytics Platform, from how bollers access and find data, to how they visualise and share it, to how AI-powered interfaces increasingly do the querying for them.

•Lead the strategy for bol’s visualisation toolchain (Tableau, Looker Studio) diagnosing today’s fragmentation, defining what good looks like, and building toward a landscape that’s coherent, trusted, and self-serve by design.

•Define and drive the product vision for agentic analytics capabilities, including conversational interfaces, AI-driven insight generation, and automation of high-frequency analytical workflows.

•Lead the discovery of use-case specific capabilities and build a repeatable playbook for what comes next by translating use-case needs into scalable platform capabilities.

•Translate the complexity of LLM integration, metrics layers, and data pipelines into a clear, phased roadmap that engineering, analytics, and business stakeholders can all rally behind.

•Work directly with Analytics Engineering, Data Engineering and Agentic Operations Platform teams to ensure the agentic layer sits on a trustworthy, governed data foundation, not quicksand that leads to hallucination at scale.

•Navigate a highly federated data organisation: align dozens of domain teams, analytics engineers, and senior stakeholders across commercial, operational, legal, and technical functions, without central control as your lever.

•Define what “good” looks like across the whole platform: evals such as task success rates, visualisation adoption, time-to-insight, analyst time reclaimed, and instrument the systems to actually measure it.

•Champion a test-and-learn approach: run controlled experiments, learn from field tests, and know when to scale and when to kill.

•Drive internal enablement: making sure that when you build something, teams actually use it, trust it, and tell you what to build next.