Senior Data Architect

Define and maintain enterprise architecture strategy aligning business objectives with technology capabilities, ensuring scalability, reliability, security, and compliance. Lead, mentor, and scale a team of solution and enterprise architects, fostering a high-performance culture rooted in architectural excellence, innovation, and collaboration. Lead architecture governance and standards, establishing frameworks, best practices, and review designs and processes across applications, data, interfaces, and infrastructure domains. Drive cross-functional alignment by collaborating with business, IT, and engineering leaders to ensure technology roadmaps support organizational priorities and innovation. Build the Enterprise Architecture team, fostering strong architectural excellence, knowledge sharing, and continuous improvement across the enterprise. Evaluate emerging technologies and guide strategic investments in data platforms, AI tools, interfaces and automation to enhance healthcare efficiency and outcomes. Build relationships with partners such as Microsoft, AWS and Service delivery partners to execute on the enterprise architecture vision and strategy. Experience: 12+ years in data architecture, with at least 3-5 years in an enterprise or solution architecture leadership capacity. Leadership: Proven experience building and leading cross-functional enterprise architecture teams and influencing enterprise-wide technology direction. Expertise in semantic data modelling and data engineering skills. Experience in architecture frameworks, best practices, designs and processes across applications, data, interfaces, and infrastructure domains. ⁠Strong experience with data platforms such as Snowflake, Databricks, Microsoft Fabric, or Azure Data Gateway, Azure Synapse. BI Tools: Working Knowledge in Power BI and integration with Microsoft Fabric is a plus. Data Integration: Experience with interfaces, API designs and 3rd party systems integration using tools such as Boomi, Azure API Management, and other middleware platforms. Data Engineering: Proficient in designing ELT/ETL processes using SQL Server, columnar data format such as Delta or Parquet and Fabric Pipelines. Architecture: Strong understanding of medallion architecture, data virtualization principles, cloud-based data management, and analytics technologies. AI: Working knowledge of AI tools to accelerate development is a plus, such as Github Copilot, Cursor AI, Claude or similar. Programming: Expertise with Python, T-SQL, Spark or other scripting languages for data transformation. Experience in programming languages such as C#, .NET platform, Node.js, Vue.js will be preferable Methodologies: Agile/Scrum project delivery experience. Communication: Exceptional communication, strategic thinking, and stakeholder management skills; ability to bridge technical and business domains effectively. Design and implement scalable BI and data architecture using Data Lake or Lakehouse paradigms, including medallion architecture. Architect and optimize ELT/ETL pipelines using SQL Server, Dataflows, and data Pipelines. Develop a robust semantic layer and underlying data model to bridge technical data and business language, applying data virtualization principles. Design and optimize semantic models that represent data in a way that enhances understanding and usability for analytics. Build and manage data pipelines, Lakehouses, Data Lakes, and Data Warehouses in Azure or AWS. Manage Power BI dashboards with advanced DAX and real-time data integration. Implement data governance, security, and compliance best practices. Ensure data quality, lineage, and availability across all reporting layers.