Data Architect

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 SQL, Synapse and/or Microsoft Fabric, Azure Data Factory or equivalent pipeline tooling.
• Data modeling across layers: dimensional (Kimball) for serving, plus an integration-layer methodology (medallion/lakehouse or Data Vault).