Data Architect(AI)
AI Data Architect
InvoiceCloud
Employment Type: Full-Time
Role Summary
The AI Data Architect is responsible for designing, evolving, and governing InvoiceCloud's
production data ecosystem to ensure it is secure, scalable, and optimized for current and future
AI-driven use cases. This role focuses on making our 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.
What Success Looks Like
Success in this role means InvoiceCloud's data is trusted, discoverable, well-modeled, and
performant for AI consumption. AI teams and product engineers can confidently leverage
production data without excessive preprocessing, data quality issues, or architectural
constraints. Data pipelines, storage patterns, and search/indexing strategies are intentionally
designed to support evolving AI workloads while maintaining operational excellence, security,
and compliance.
Key Responsibilities
1. Design and evolve enterprise data architectures that support AI, analytics, and search
workloads.
2. Ensure Azure SQL DB schemas, indexing strategies, and data models are optimized for AI
and feature generation.
3. Evaluate and recommend appropriate Azure services and patterns to enable AI data usage.
4. Design data access and synchronization strategies across relational, NoSQL, and search
platforms.
5. Establish data quality, governance, lineage, and lifecycle standards.
6. Collaborate with engineering, product, and security teams to align data architecture with AI
initiatives.
7. Document architecture decisions, data flows, and operational standards.
Required Skills & Experience
12 to 16 Years
1. Strong expertise with Microsoft SQL Server and Azure SQL Database.
2. Advanced T-SQL, data modeling, and query optimization skills.
3. Experience designing data architectures for AI and analytics workloads.
4. Strong understanding of Azure data services and cloud-native architectures.
5. Excellent communication and collaboration skills.
6. 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.
7. Lead cross-functional, global discovery and solution design aligned to business priorities.
8. Provide technical leadership across all data infrastructure, including production systems, data warehousing, and AI/ML environments.
9. Establish standards and governance to ensure scalability, performance, and long-term evolution of the enterprise data platform.
