Solution Architect - Data & Analytics

Job Summary: We are looking for an experienced Solution Architect to design and deliver scalable, secure, and future-ready Data & Analytics platforms on Azure. The role will focus on architecting enterprise-grade data solutions supporting reporting, analytics, AI/ML, and GenAI initiatives while ensuring compliance, governance, performance, and cost optimization. Key Responsibilities: - Design scalable, secure, and high-performance data platform solutions. - Architect data ingestion, transformation, storage, governance, and consumption layers. - Review functional and non-functional design documents and ensure alignment with enterprise standards. - Collaborate with business, engineering, security, enterprise architecture, and platform teams. - Ensure data solutions comply with architecture, cybersecurity, regulatory, and governance requirements. - Drive Databricks vendor engagements and Unity Catalog adoption across projects. - Support AI/ML, MLOps, RAG, and GenAI architecture use cases on enterprise data platforms. - Identify technical risks, resolve challenges, and support timely delivery of project milestones. - Optimize platform performance and cloud cost through continuous improvement. Mandatory Skills: - Azure Data Lake / ADLS Gen2 - Databricks and Unity Catalog - Data Architecture and Data Modeling - Data Integration and Enterprise Data Platform Design - SQL for analysis, validation, modeling, and performance optimization - Data Security, Governance, Metadata Management, and Lineage Preferred Skills: - Python and PySpark - Azure Synapse and Cloud-Native Architecture - AI/ML Lifecycle and MLOps - RAG and GenAI Solution Design - Terraform / Infrastructure as Code - Agile, DevOps, Cost Optimization, and Stakeholder Management Required Experience: - 10+ years of overall experience preferred, with a strong background in data architecture and engineering. - Proven experience designing and implementing large-scale Azure-based data platforms. - Strong expertise in Azure Data Lake, Databricks, SQL, data modeling, and integration patterns. - Experience with metadata management, data governance, secure data access, and architecture governance. - Ability to translate business requirements into robust, scalable, and cost-effective technical solutions. - Strong communication skills with experience working with senior business and technology stakeholders. Good to Have: - Hands-on experience with AI/ML solution architecture and model lifecycle awareness. - Knowledge of MLOps, model deployment, monitoring, and operational governance. - Exposure to RAG, GenAI, Terraform, ARM templates, Python, PySpark, Synapse, Agile, and DevOps