Senior Data Lead - Product

About the role

Schibsted Media builds news products used by more than 6 million people across the Nordics, and data is central to personalization, user engagement and monetization. As a new role, the Senior Data Lead joins the Product Data teams (User, Content and Performance) and works directly alongside the Director of Data, with wide influence across analytics, data engineering and data science. The aim is to set shared direction and standards across the three domains.

Key responsibilities

• Be accountable for Product Data as a discipline and lead cross-product data initiatives that no single domain can own.
• Define technical and analytical direction with the Director of Data and act as a reference for analysts, engineers and data scientists.
• Build an operating model and a shared playbook of standards, guidelines and reusable patterns for owning, documenting and keeping data products reliable.
• Lead a small number of cross-domain, cross-brand initiatives such as shared KPIs and user segments.
• Explore and scale AI use in data work, from better analyst and engineer tools to agents, and ensure data is ready for AI-powered product features.
• Partner with central Data and AI teams on governance, architecture and shared standards, bringing Product's needs into shared decisions.
• Coach and develop senior and lead individual contributors across the three disciplines, without line management.

Required profile

• 7+ years of experience in analytics, data engineering or data science, including several years in senior or lead roles.
• Experience building shared ways of working, standards and best practice across several teams.
• Experience designing metrics and KPIs that hold across brands, products or markets.
• Applied experience using AI and LLM tooling; depth in some areas is a strong plus.
• Plus: fluent Swedish or Norwegian, media experience, or experience translating data work for executive and board-level audiences.

Required skills

• Deep expertise in at least one of analytics, data engineering or data science, with enough fluency in the others.
• Strong SQL and data modelling, hands-on dbt and a cloud warehouse such as Snowflake.
• Strong Python or R.
• Plus: data mesh and data product practices, data governance, GDPR, experiment design and causal inference, ML/AI evaluation standards.

The tech stack includes AWS, Snowflake, dbt, Airflow, Python, SQL, R, Amplitude, Superset, Streamlit, Kilkaya, Pulse and Kafka. Applicants send a CV and a letter of motivation; selection happens throughout the process.