Global Head of Data Solutions and Engineering (Hybrid, Copenhagen)

Job summary

This role is essential for shaping the enterprise data foundation, providing architectural direction, operational discipline, and leadership to scale data capabilities and support analytics, AI, and business transformation.

Qualifications

• Over 10 years of technical experience in data engineering and architecture, with a strong background in building enterprise-grade data platforms in cloud environments.
• At least 5 years of leadership experience, particularly in managing distributed engineering teams.
• Deep expertise in modern data stacks, including Databricks and Azure, with a focus on pipeline orchestration and automation.
• Strong understanding of AI integration with data stacks and collaboration with data scientists and AI/ML engineers.
• Knowledge of governance, security, and compliance to ensure data quality and operational reliability.
• Excellent communication skills to convey technical concepts to senior stakeholders.

Responsibilities

• Own and evolve the enterprise data platform architecture, ensuring scalability and reliability.
• Develop core data engineering capabilities, including data ingestion frameworks and CI/CD pipelines.
• Translate architectural strategies into reusable engineering components for various business domains.
• Deliver production-ready data products for analytics and AI/ML use cases.
• Collaborate with cross-functional teams to align designs with functional requirements and governance standards.
• Define engineering standards to improve development quality and platform adoption.
• Maintain operational excellence regarding platform reliability and data privacy.
• Drive continuous optimization of platform performance and cost management.
• Lead and develop high-performing engineering teams across multiple locations.

Skills

• Proficiency in modern data stacks and cloud technologies.
• Strong leadership and team management skills.
• Ability to translate technical strategies into business outcomes.

Education

• Relevant degree in Computer Science, Engineering, or a related field is preferred.

Tools

• Familiarity with data engineering tools such as Databricks, Azure, and Infrastructure-as-Code solutions.