Azure Data Architect

Role Overview

Data Architect with Azure supporting Platform Architecture & Governance team

Main activities and responsibilities:

• Collaborate with product, data engineering, security and network teams to

translate business needs into Azure data solutions.

• Define and maintain reference architectures and reusable solution blueprints for

common data use cases.

• Continuously update platform standards based on Azure roadmap and new

service capabilities including the technology selection.

• Ensure solutions comply with enterprise network standards (VNet design,

subnets, NSGs, routing, firewalls).

• Design and enforce private connectivity patterns (Private Link, private endpoints,

VNet integration, ExpressRoute/VPN).

• Embed security by design in all blueprints (identity and access management,

encryption, secrets and key management).

• Integrate AI and advanced analytics into the platform approach

• Establish standards for data models, naming conventions, metadata, data

quality and lifecycle management

• Lead or contribute to architecture/design reviews, ensuring adherence to

platform and security standards.

• Define monitoring, logging and observability patterns (Azure Monitor, Log

Analytics, security tooling).

Required competences:

• Deep hands-on experience with core Azure data services (Data Lake,

Synapse/Fabric, Databricks, Data Factory, Event Hub, Azure SQL, etc.).

• Strong understanding of Azure networking (VNets, subnets, NSGs, firewalls,

routing, Private Link, ExpressRoute/VPN, hybrid connectivity).

• Solid knowledge of cloud security concepts (Entra ID, RBAC, least privilege,

encryption, Key Vault, secrets management).

• Proficiency with Infrastructure as Code tools (Bicep, Terraform or ARM) and

CI/CD practices.

• Strong data architecture skills (data modeling for lakehouse/warehouse,

integration patterns, data quality and lifecycle).

• Experience designing platforms that support AI/ML and generative AI workloads,

including data preparation, model lifecycle, and integration of AI services (e.g.

Azure Machine Learning, Azure OpenAI, Fabric).

• Understanding of responsible AI principles (governance, transparency, data

protection, bias mitigation) and how to embed them into platform patterns.

• Ability to create clear, reusable reference architectures, design documents and

technical guidelines.

• Strong stakeholder management and communication skills to align with product,

engineering, security and network teams.

• Architectural thinking and decision-making skills, balancing security, cost,

performance and time-to-market