Head of Data Products Engineering

A role for a senior product and engineering leader who can turn Schibsted's data assets into durable, governed, AI-ready products. The remit spans central Schibsted data products and corporate data domains, with a strong bias for reuse, clarity of ownership, and measurable business impact.

About the team

Data Product Engineering sits inside the Data & AI organization and builds the data products, schemas, APIs, metadata, and delivery patterns that make Schibsted's data usable across brands, corporate functions, and AI-powered products. The team turns fragmented assets into shared products with clear ownership, contracts, telemetry, and quality standards. The mandate covers reusable central Schibsted data products, as well as corporate domains such as Finance and HR, and is closely linked to the 2026 ambition to make our content and data modular, machine-readable, rights-aware, and reusable.

The role is a key lever for simplifying the platform, strengthening trust in data products, and accelerating product delivery without adding unnecessary complexity.

What you will do

· Define and own the vision, roadmap, and operating model for Data Product Engineering, translating Data & AI strategy into a clear and prioritized delivery portfolio.

· Lead the design and evolution of reusable data products and interfaces that serve editorial, product, commercial, finance, HR, and AI use cases.

· Set the engineering standards for data product design, including schemas, contracts, metadata, lineage, quality, observability, documentation, and service levels.

· Partner closely with Data Architecture, Data Infrastructure, AI Foundations, Enablement, Security, Privacy, Legal, and business stakeholders to deliver shared, governed solutions.

· Build a strong intake, prioritization, dependency, and support model so the team can deliver reliably at scale.

· Coach and grow a team of engineers and product-minded technologists, setting a high bar for clarity, execution, and collaboration.

· Drive the shift from one-off data assets to durable products that are easy to discover, trust, reuse, and extend.

· Help shape the AI-ready foundation for retrieval, agents, personalization, signal routing, and content supply use cases.

· Balance speed, quality, cost, and simplification in buy-versus-build and platform decisions, with a pragmatic focus on value.

Who you are

· A strong technical leader with deep data engineering or data platform experience and a track record of shipping products or platform capabilities.

· An experienced manager or team lead who builds trust, creates clarity, and develops people.

· Comfortable operating at both portfolio and implementation level, and able to translate strategy into practical execution.

· Effective across technical and business stakeholders, with good judgment in ambiguous situations.

· Structured in how you think about governance, prioritization, and trade-offs, while still moving fast enough to create momentum.

· Direct, collaborative, and crisp in communication.

Nice to have

· Experience in media, digital products, or other content-rich businesses.

· Background in event pipelines, semantic layers, metadata/catalog tooling, or AI/ML enablement.

· Experience with rights-aware data products, agent access patterns, or retrieval-backed AI products.

· A strong instinct for making platforms easy to use, reusable, and measurable.

What success looks like

· Central and corporate data products are adopted, trusted, and easy to reuse.

· Standards, ownership, and operating rhythms reduce duplication and rework.

· Data products are ready for AI use cases and governed access is built in rather than bolted on.

· Stakeholders see faster delivery, clearer ownership, and better business impact from the team.