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About the role: We're looking for a hands-on Data Platform Architect to own the architecture, delivery, and governance of Auditdata's group-wide data platform, built on Azure. You'll design and build a single, reliable analytical ecosystem that consolidates data from multiple Auditdata products — starting with Practice Management Software — across countries and regions, powering reporting, analytics, and data-driven decision-making group-wide. This role combines strategic ownership with hands-on execution: you'll set the architectural direction and also build it yourself. Two early mandates will define your first months: Define the target data platform strategy within the Azure ecosystem (Azure SQL, Synapse, Microsoft Fabric/OneLake) and own the roadmap from current state to target architecture. Design the cross-product consolidation architecture — establishing how data from independently built products, spanning different regions and legal jurisdictions, converges into a single governed platform. What you'll do: Data Platform Architecture: Define and evolve the group data platform architecture - evaluate and select the target Azure stack (Azure SQL, Synapse, Fabric) and own the migration roadmap to it. Design the multi-source integration architecture: ingestion from multiple products with heterogeneous schemas, using an integration-layer pattern suited to many sources (medallion/lakehouse or Data Vault) beneath a dimensional serving layer. Address multi-region and data residency requirements: regional hosting, cross-border transfer constraints, GDPR and local privacy law, tenant and country-level data isolation. Collaborate with Infrastructure Architects on storage, compute, performance, and Azure cost optimization across regions. Data Modeling & Master Data: Design and maintain the three modeling layers: raw/integration (multi-source harmonization), canonical/master data (shared entities such as clinic, patient, device, product across products and countries), and serving (Kimball fact/dimension models optimized for Power BI). Define master data management approach: entity matching, survivorship, and golden-record rules across products. Optimize analytical models for performance (incremental refresh, partitioning, query tuning). Data Engineering (hands-on): Design, build, and maintain ETL/ELT pipelines consolidating data from product services into the platform. Build and tune Power BI semantic models and support dashboard development. Implement data quality, lineage, and observability - monitoring, alerting, reconciliation checks across sources. Integration & Governance: Collaborate with Domain Architects and product teams to define data contracts and export interfaces from operational services. Govern data ingestion - accuracy, performance, and alignment with security and privacy policies. Establish and run group data governance: ownership, definitions (a shared business glossary across products), metadata management, documentation, and schema versioning. Contribute to ADRs, data architecture diagrams, and data governance documentation. Data Migration (secondary): Support customer data migration into Manage from legacy systems - mapping, transformation, and validation approaches. Define reusable migration tooling and quality gates in collaboration with onboarding/delivery teams. Stakeholders: Work with business stakeholders across products and countries to translate analytical needs into platform capabilities and data models. What you bring: Must-haves: 7+ years in data engineering/architecture , including end-to-end ownership of a production data platform or warehouse. Proven experience consolidating data from multiple heterogeneous source systems into one analytical platform. Deep Azure data ecosystem expertise: Azure S