Data Architect

Data Architect

Role Overview

We are looking for an experienced Data Architect to shape and evolve an enterprise data platform that enables reliable data integration, advanced analytics, and data-driven business decisions. The role combines architectural design with technical leadership, ensuring that data solutions are scalable, secure, well-documented, and aligned with business needs. You will establish architectural principles and reusable design patterns while collaborating with engineering teams and business stakeholders to support the platform’s long-term development.

Key Responsibilities

• Design and evolve the end-to-end data architecture, including data models, ingestion patterns, transformation layers, data products, and consumption interfaces.
• Define architectural principles and technical standards for data storage, processing, integration, access, and sharing.
• Design scalable data integration solutions covering batch and streaming workloads, ETL/ELT processes, and connections between source systems and analytical environments.
• Establish best practices for data modelling, metadata management, data lineage, reference data, and business terminology.
• Maintain architectural documentation, including data models, integration patterns, design decisions, standards, and reference materials.
• Translate business and technical requirements into architectural designs, data structures, and implementation recommendations.
• Conduct architecture reviews, document key decisions, and guide delivery teams in applying agreed standards and security principles.
• Work closely with Data Engineers, Analysts, Data Owners, Project Managers, and business stakeholders to ensure solutions meet organizational requirements.
• Present architectural recommendations and platform development options to both technical and business audiences.

Required Experience and Skills

• Proven experience designing and developing scalable enterprise data platforms based on Lakehouse architecture.
• Strong knowledge of Azure Databricks and modern cloud-based data processing environments.
• Experience creating conceptual, logical, and physical data models.
• Practical knowledge of data integration, including ETL/ELT, batch processing, streaming, and data exchange between operational and analytical systems.
• Experience defining architectural standards, reusable patterns, and best practices for data modelling, integration, security, and data access.
• Strong understanding of data architecture principles and the relationship between data platforms, reporting, and analytics.

Data Modelling and Architecture Documentation

• Advanced knowledge of data modelling techniques, including ERD and UML.
• Experience using Enterprise Architect or comparable tools for documenting data models, architectures, and data flows.
• Ability to manage and maintain an architecture repository containing data models, standards, architectural decisions, integration patterns, and reference documentation.

Technology Environment

• Microsoft Azure cloud ecosystem and services supporting enterprise-scale data platforms.
• Azure Databricks for data engineering and Lakehouse processing.
• Azure Data Lake Storage and Delta format for data storage.
• Microsoft Fabric for analytics and data consumption.
• Apache Airflow for workflow orchestration.
• Unity Catalog for data discovery, access control, schema management, and governance.
• Databricks Asset Bundles for managing and deploying data workflows.
• Delta Sharing for secure data exchange across environments.
• Terraform and Infrastructure as Code for automating cloud and platform resources.
• Azure DevOps for source control and CI/CD processes.

Nice to Have

• Familiarity with data management and enterprise architecture frameworks, such as DAMA-DMBOK and TOGAF.