Data Architect (AI)

Role Overview: As an AI Data Architect at InvoiceCloud, you will be responsible for designing, evolving, and governing InvoiceClouds production data ecosystem to ensure it is secure, scalable, and optimized for current and future AI-driven use cases. Your focus will be on making the Azure-hosted production data fully AI-ready, while evaluating and recommending the best Azure-native and complementary technologies to enable advanced analytics, machine learning, and generative AI capabilities. Success in this role means ensuring that InvoiceClouds data is trusted, discoverable, well-modeled, and performant for AI consumption. Key Responsibilities: - Design and evolve enterprise data architectures that support AI, analytics, and search workloads. - Ensure Azure SQL DB schemas, indexing strategies, and data models are optimized for AI and feature generation. - Evaluate and recommend appropriate Azure services and patterns to enable AI data usage. - Design data access and synchronization strategies across relational, NoSQL, and search platforms. - Establish data quality, governance, lineage, and lifecycle standards. - Collaborate with engineering, product, and security teams to align data architecture with AI initiatives. - Document architecture decisions, data flows, and operational standards. Qualification Required: - 12 to 16 years of experience in the field. - Strong expertise with Microsoft SQL Server and Azure SQL Database. - Advanced T-SQL, data modeling, and query optimization skills. - Experience designing data architectures for AI and analytics workloads. - Strong understanding of Azure data services and cloud-native architectures. - Excellent communication and collaboration skills. - Define and drive a modern data architecture strategy beyond traditional MS SQL Server, including cloud-native, distributed SQL and NoSQL platforms (e.g., MongoDB) and AI-ready data systems. - Lead cross-functional, global discovery and solution design aligned to business priorities. - Provide technical leadership across all data infrastructure, including production systems, data warehousing, and AI/ML environments. - Establish standards and governance to ensure scalability, performance, and long-term evolution of the enterprise data platform. Role Overview: As an AI Data Architect at InvoiceCloud, you will be responsible for designing, evolving, and governing InvoiceClouds production data ecosystem to ensure it is secure, scalable, and optimized for current and future AI-driven use cases. Your focus will be on making the Azure-hosted production data fully AI-ready, while evaluating and recommending the best Azure-native and complementary technologies to enable advanced analytics, machine learning, and generative AI capabilities. Success in this role means ensuring that InvoiceClouds data is trusted, discoverable, well-modeled, and performant for AI consumption. Key Responsibilities: - Design and evolve enterprise data architectures that support AI, analytics, and search workloads. - Ensure Azure SQL DB schemas, indexing strategies, and data models are optimized for AI and feature generation. - Evaluate and recommend appropriate Azure services and patterns to enable AI data usage. - Design data access and synchronization strategies across relational, NoSQL, and search platforms. - Establish data quality, governance, lineage, and lifecycle standards. - Collaborate with engineering, product, and security teams to align data architecture with AI initiatives. - Document architecture decisions, data flows, and operational standards. Qualification Required: - 12 to 16 years of experience in the field. - Strong expertise with Microsoft SQL Server and Azure SQL Database. - Advanced T-SQL, data modeling, and query optimization skills. - Experience designing data architectures for AI and analytics workloads. - Strong understanding of Azure data services and cloud-native architectures. - Excellent communication and collaboration skills. - Define and drive a modern data architecture strategy beyond traditional MS SQL Server, including cloud-native, distributed SQL and NoSQL platforms (e.g., MongoDB) and AI-ready data systems. - Lead cross-functional, global discovery and solution design aligned to business priorities. - Provide technical leadership across all data infrastructure, including production systems, data warehousing, and AI/ML environments. - Establish standards and governance to ensure scalability, performance, and long-term evolution of the enterprise data platform.