Data Architect

Role - Data Architect

Client - UHG

• Own the overall architecture and design of the enterprise data, semantic, and knowledge ecosystem.
• Define enterprise-wide data architecture principles, standards, patterns, and reference architectures .
• Design scalable data platforms and architectures across cloud, lakehouse, warehouse, and modern data ecosystem environments.
• Develop and govern the enterprise semantic layer , including business definitions, metrics, KPIs, dimensions, hierarchies, and relationships.
• Establish architecture for knowledge models, metadata, business glossaries, ontologies, and knowledge graphs where appropriate.
• Partner with Finance, Accounting, FP&A, Risk, and other business functions to translate domain requirements into reusable data and semantic models.
• Define strategies for Master Data Management (MDM) , data quality, lineage, metadata management, and data lifecycle management.
• Establish data governance architecture aligned with enterprise policies, regulatory requirements, security, privacy, and compliance standards.
• Design AI-ready data architecture patterns that support machine learning, GenAI, retrieval-augmented generation (RAG), intelligent automation, and advanced analytics.
• Ensure data is discoverable, trusted, contextualized, interoperable, and fit for both traditional analytics and AI applications.
• Evaluate and recommend technologies and platforms such as Snowflake, Databricks, Azure , or comparable cloud data platforms.
• Define integration patterns across ERP, finance systems, operational applications, data warehouses, data lakes/lakehouses, APIs, and external data sources.
• Establish architecture for real-time, batch, event-driven, and streaming data use cases as required.
• Drive adoption of reusable data products, domain-oriented architecture, and self-service data capabilities .
• Collaborate with enterprise architects, solution architects, data engineers, data scientists, analysts, and business stakeholders.
• Provide technical leadership and architectural guidance for major data and AI transformation initiatives.
• Assess existing architecture, identify gaps, and develop target-state architecture and transformation roadmaps .
• Ensure architectural decisions balance scalability, performance, security, cost, maintainability, and business value.
• Strong experience in enterprise data architecture and large-scale data transformation programs.
• Deep understanding of data modeling, data integration, data platforms, metadata, lineage, and data governance .
• Strong knowledge of finance and accounting data domains , including concepts such as GL, chart of accounts, AP, AR, fixed assets, revenue, financial reporting, budgeting, and forecasting.
• Hands-on or architectural experience with Snowflake, Databricks, Azure , or comparable modern cloud data platforms.
• Experience designing enterprise semantic layers and business-oriented data models.
• Strong understanding of Master Data Management (MDM) and reference data management.
• Experience establishing enterprise data governance, data quality, metadata, lineage, and business glossary capabilities.
• Understanding of modern AI/ML and GenAI architecture patterns , including vector search, embeddings, RAG, knowledge graphs, and AI-ready data foundations.
• Experience with data architecture patterns such as data lake, data warehouse, lakehouse, data mesh, data fabric, and data products .
• Strong understanding of cloud architecture, APIs, integration patterns, security, and data privacy.
• Ability to communicate complex technical concepts effectively to both technical and senior business stakeholders.