Data Warehouse Architect - Mehiläinen Oy - Distans - Globalt

At Mehiläinen, we are on a journey towards an ambitious goal: to be the leading healthcare company in Europe by 2030. Our growth story has already taken us to seven countries, driven by strategic decisions and a commitment to data-driven insights. Do you want to work in an inspiring work community in a leading European social and healthcare sector company? If your answer is yes, you could be a new member of our team! We are looking for an experienced Data Warehouse Architect to join our growing team. In this role, you will be shaping our data landscape, ensuring that our business stakeholders have access to reliable, high-quality data for critical decision-making. You will bridge the gap between business needs and technical implementation, designing robust data solutions supporting our growth. In this role, you will be instrumental in shaping a group-wide data environment by bringing together diverse international solutions and guiding the transition from existing local platforms into a unified, modern ecosystem. As a Data Warehouse Architect, you will play a key role in enabling business growth and data-driven decision-making. You will be leading the end-to-end lifecycle of enterprise data warehousing solutions, ensuring scalable, secure, and high-performing platforms that meet both business and technical needs. A deep understanding of business requirements is expected, as you will combine architectural vision with hands-on development. Your work will directly contribute to our ability to analyze performance, identify opportunities, and drive strategic initiatives across the Mehiläinen Group. Key Responsibilities include: Solution Planning for Business Demand: Partner with business stakeholders to understand strategic and operational reporting & analytics needs, proactively shaping data solutions that enable decision-making and analytics. Translate business demand into architectural blueprints, solution designs, and prioritized implementation roadmaps. Drive the definition and evolution of KPIs, data products, and analytical capabilities as part of a service-oriented data platform. Data Warehouse Architecture & Solution Design: Development and ownership of the end-to-end data warehouse architecture, defining scalable structures, data models, and integration patterns aligned with business strategy. Design, govern, and continuously optimize logical and physical data models, leveraging AI capabilities to enhance data quality, consistency, automation, and trusted data assets across the enterprise platform. Acting as the architectural authority for data solutions, evaluating new technologies and shaping the long-term data ecosystem. Data Integration & Processing Frameworks: Design and oversee robust, scalable data integration frameworks (ETL/ELT), ensuring efficient, reliable, and maintainable data pipelines. Multi-Platform Integration: Design and govern integration patterns that allow decentralized teams (using e.g., Apache/Open Source tools) to contribute to and benefit from the group-wide OneLake environment. Guide development teams in implementing best practices for data ingestion, transformation, and orchestration across diverse source systems. Cross-Functional Collaboration: Collaborate closely with group wide business units, analysts, data engineers and IT teams to ensure seamless data flow and alignment between architecture and implementation. Act as a key interface between business and technology, ensuring solutions are both technically sound and business-relevant. Data Governance, Security & Compliance: Establish, implement, and continuously develop the governance framework, standards, and operating principles for data governance, security, and compliance across the data platform. Federated Governance: Implement a "Hub and Spoke" governance model that balances group-level standards with the necessary local autonomy for our international business units. Ensure data is managed as a strategic business asset through clear ownership models, end-to-end lineage, robust quality controls, and effective stewardship practices that support trusted decision-making and operational excellence. Drive governance processes and controls that secure regulatory compliance, reduce risk, and ensure consistent, high-quality data and reliable data operations across the organization. AI & Advanced Analytics Enablement: Define and drive the integration of AI and machine learning capabilities within the data platform, ensuring the data warehouse supports scalable and production-ready AI use cases. Collaborate with data science and analytics teams to design architectures that enable model development, deployment, and monitoring. Ensure data readiness for AI (e.g., feature engineering, data quality, lineage, and governance) and promote reuse through standardized data products and feature stores. Performance, Scalability & Continuous Improvement: Architect and continuously optimize the platform for performance, scalability, and cost-efficiency in line with growing data volumes and usage. Drive continuous improvement of data services, enabling a modern, flexible, and high-performing data warehouse ecosystem.