About this role
Job Summary
• We are seeking an experienced and highly skilled Data Architect with strong expertise in Microsoft Fabric, Azure Data Platform, Databricks, and Apache Spark to lead the design and implementation of enterprise-scale data modernization initiatives. • The ideal candidate will have extensive experience architecting cloud-native data platforms, defining data strategies, and delivering scalable, secure, and high-performing data solutions across complex business environments. • This role requires deep technical knowledge of Microsoft Azure data services, modern data architecture patterns, data governance frameworks, and large-scale analytics platforms. Experience within the Insurance domain is highly desirable.
Key Responsibilities Data Architecture & Strategy
• Define and drive enterprise data architecture strategies aligned with business objectives and technology roadmaps. • Design scalable, secure, and high-performance data platforms leveraging Microsoft Fabric, Azure, and Databricks ecosystems. • Establish architecture standards, best practices, reusable design patterns, and governance frameworks for enterprise data solutions. • Lead cloud data modernization and migration initiatives from legacy platforms to cloud-native architectures. • Create architecture blueprints, reference architectures, and implementation roadmaps. Microsoft Fabric Leadership
• Architect and implement end-to-end solutions using: • Microsoft Fabric Lakehouse • Fabric Data Warehouse • Data Pipelines • Real-Time Analytics • OneLake • Power BI Integration
• Design Medallion Architecture (Bronze, Silver, Gold layers) within Microsoft Fabric. • Drive Fabric deployment strategies, performance optimization, and environment governance. • Implement Fabric security, access controls, lineage, monitoring, and data lifecycle management. Azure Data Platform Architecture
• Design and implement enterprise data solutions using: • Azure Data Factory (ADF) • Azure Data Lake Storage Gen2 (ADLS) • Azure Synapse Analytics • Azure Key Vault • Azure Event Hub • Azure Functions • Azure DevOps
• Define ingestion, transformation, storage, and consumption frameworks. • Drive cloud adoption and optimization initiatives. Databricks & Big Data Engineering
• Architect and optimize Databricks Lakehouse solutions. • Lead implementation of: • Apache Spark • PySpark • Delta Lake • Unity Catalog • Databricks Workflows • Auto Loader • Delta Live Tables
• Design large-scale data pipelines supporting batch and real-time workloads. • Optimize Spark processing, cluster utilization, and workload performance. Data Governance & Security
• Define enterprise data governance standards. • Implement metadata management, lineage, cataloging, and data quality frameworks. • Partner with security teams to ensure data privacy, compliance, and regulatory adherence. • Enable business-friendly data discovery and self-service analytics. Stakeholder Engagement
• Collaborate with business leaders, product owners, engineering teams, and program stakeholders. • Translate business requirements into scalable technical architectures. • Lead architecture reviews, design workshops, and solution governance forums. • Provide technical leadership and mentorship to data engineering teams
Required Qualifications
• Bachelor's or Master's degree in Computer Science, Information Systems, Engineering, or related field. • Minimum 10 years of experience in Data Engineering, Data Architecture, or Data Platform leadership roles. • Strong experience designing and architecting data solutions in cloud environments. Must-Have Technical Skills Microsoft Fabric
• Fabric Lakehouse • Data Warehouse • OneLake • Data Pipelines • Fabric Security & Governance • Real-Time Analytics • Power BI Integration Microsoft Azure
• Azure Data Factory (ADF) • Azure Data Lake Storage (ADLS Gen2) • Azure Synapse Analytics • Azure Key Vault • Azure Functions • Azure Event Hub • Azure DevOps Databricks
• Azure Databricks • Delta Lake • Unity Catalog • Databricks Workflows • Delta Live Tables • Auto Loader Data Engineering
• Apache Spark • PySpark • SQL • Python • Data Modeling • ETL/ELT Design Architecture & Governance
• Medallion Architecture • Lakehouse Architecture • Data Mesh (Preferred) • Data Governance • Metadata Management • Data Lineage • Data Quality Frameworks • CI/CD & DevOps Practices