Data Architect

Transport is at the core of modern society. Imagine using your expertise to shape sustainable transport and infrastructure solutions for the future. If you seek to make a difference on a global scale, working with next-gen technologies and the sharpest collaborative teams, then we could be a perfect match.

What you will do

We are building a new Data & AI team focused on delivering end-to-end use cases, from onboarding data onto our Azure-based platform and building Data Products to enabling advanced analytics and AI solutions. We are looking for a Data Architect who is curious, open-minded and comfortable working across different domains and technologies. This role requires someone who can bring people together, challenge assumptions, ask the right questions and provide architectural leadership as we expand our Data & AI capabilities.

Key Responsibilities

• Provide strategic architectural leadership for the Data and AI landscape, taking an end-to-end view across business, data, technology, analytics and AI solutions.
• Define and drive the target architecture and long-term technology direction for the Data and AI, ensuring scalability, integration, reuse and sustainable growth.
• Shape effective use of our modern data platform based on Azure and Databricks, enabling trusted, governed and reusable data to support Data Products, analytics and AI solutions.
• Establish architectural principles and ensure alignment with Volvo Group Data Model, Volvo Enterprise Reference Architecture, enterprise standards and security requirements, while translating them into pragmatic solutions for the Volvo environment.
• Identify and manage cross-domain dependencies, architectural risks and opportunities, ensuring consistency and coherence across Data and AI ecosystems.
• Act as a trusted strategic advisor to senior business and technology stakeholders, influencing decisions and challenging existing approaches where needed.
• Translate complex business needs into clear, scalable and implementable architectural strategies, balancing innovation with governance, security, cost and technical feasibility.
• Lead and orchestrate collaboration between business stakeholders, Data Product teams, architects, engineers and external partners, creating alignment across multiple initiatives.
• Drive architectural maturity and continuous improvement by establishing common standards, reusable patterns, and ways of working that enable the organization to scale its Data and AI capabilities.