Data Architect

Role & responsibilities

• Lead the design and architecture of enterprise-scale data platforms on Microsoft Azure.
• Translate business, analytics, and data requirements into scalable end-to-end data architectures.
• Design architectures covering data ingestion, storage, ETL/ELT, processing, governance, security, analytics, and consumption.
• Architect solutions using Azure Data Factory, ADLS, Azure Databricks, Synapse Analytics, Microsoft Fabric, and Azure SQL.
• Design modern data lake, lakehouse, and data warehouse architectures.
• Lead client discovery sessions, technical workshops, and architecture discussions.
• Present architecture options, trade-offs, recommendations, and solution designs to senior stakeholders.
• Design batch, streaming, and near-real-time data processing solutions.
• Define data modelling, integration, pipeline, security, performance, and deployment standards.
• Review data models, solution designs, pipeline approaches, and technical deliverables.
• Guide Data Engineers and Technical Leads during implementation.
• Ensure solutions meet scalability, security, reliability, performance, governance, and cost requirements.
• Identify technical risks, architecture dependencies, and performance bottlenecks early.
• Work with cloud, AI, analytics, security, and DevOps teams on integrated enterprise solutions.
• Support pre-sales, technical proposals, solution estimates, and client presentations.
• Develop reusable architecture patterns, accelerators, and technical best practices.

Preferred candidate profile

• 6+ years of experience in Data Engineering, Data Architecture, Analytics, or related roles.
• Strong hands-on experience designing enterprise data solutions on Microsoft Azure.
• Strong expertise in Azure Data Factory, ADLS Gen2, Databricks, Synapse Analytics, Microsoft Fabric, and Azure SQL.
• Experience with data lakes, lakehouses, data warehouses, and distributed data processing.
• Strong experience with ETL/ELT pipelines and enterprise data integration.
• Strong knowledge of dimensional modelling and analytical data models.
• Experience integrating cloud, on-premises, APIs, SaaS, and enterprise applications.
• Understanding of data governance, lineage, metadata, data quality, security, and access control.
• Strong SQL skills with working knowledge of Python.
• Understanding of CI/CD, DevOps/DataOps, source control, and infrastructure automation.
• Strong client-facing communication and architecture presentation skills.
• Experience leading technical workshops and guiding engineering teams.