Data Architect

Auditdata

Data Platform Architect responsible for the architecture and delivery of Auditdata's Azure-based data platform. Consolidating data across multiple products for group-wide reporting and analytics.

Posted 7/21/2026full-timeRemote • 🇵🇱 PolandSeniorLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and evolving data platform architectures, particularly within the Azure ecosystem, while ensuring compliance with data residency and privacy regulations. Proficient in data modeling, ETL/ELT pipeline development, and implementing data governance practices to support analytical needs across diverse products and regions.

Highest-signal resume keywords

Azure Data Ecosystem ExpertiseData Modeling (Kimball, Medallion, Data Vault 2.0)ETL/ELT Pipeline DevelopmentPower BI Semantic Models and DAXGDPR Compliance and Data Residency

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Hard Skills

Data EngineeringData ArchitectureData ConsolidationData Quality and LineageData GovernanceData ContractsIncremental RefreshQuery TuningMulti-Source IntegrationMaster Data Management

Soft Skills

CollaborationDocumentationAnalytical ThinkingProblem SolvingCommunication

Tools & Technologies

Azure SQLAzure SynapseMicrosoft FabricAzure Data FactoryPower BI

Industry Keywords

Data ResidencyGDPRCross-Border TransferDimensional ModelingMicroservices

Tech Stack

Tools & technologies

AzureETLSQLVault

About the role

Key responsibilities & impact

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.
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).
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.
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.
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.
Work with business stakeholders across products and countries to translate analytical needs into platform capabilities and data models.

Requirements

What you’ll need

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 2.0), and canonical/master data modeling.
Hands-on Power BI: semantic models, DAX, deployment pipelines.
Experience with multi-region or multi-country data platforms: data residency, GDPR, cross-border transfer constraints.
Experience designing data contracts/exports from distributed or microservice-based systems.
Ability to make and document architecture decisions (ADRs) and defend platform choices with trade-off analysis.

Benefits

Comp & perks

Long-term engagement in a stable, growing SaaS company
Remote-first and async-friendly with flat organizational structure
High bar for engineering and product quality
A team that values depth over hype: production quality over flashy prototypes
A culture that values personal growth as much as business outcomes