IT Support

• Develop data architecture across the organization, providing expertise, and resolving conflicts in data architecture projects by analyzing business needs and managing the quality of deliverables created.
• Drive alignment on data architecture principles, foster cross-functional collaboration, and ensure that data capabilities are scalable and aligned with enterprise AI goals.
• Design and evolve enterprise-level semantic data models, including logical and conceptual models, ontologies, and domain definitions.
• Partner with product, data, and engineering teams to define domain-driven data contracts that support AI, analytics, and operational needs.
• Collaborate with governance, AI/ML, and metadata teams to support computational governance and automated metadata enrichment.
• Evaluate AI-generated code for accuracy, performance, and clarity.
• Build data architecture and applications that ensure reporting, analytics, data science, and data management and improve accessibility, efficiency, governance, processing, and quality of data.
• Lead the standardization of metadata practices across domains, ensuring discoverability, lineage, and governance.
• Enable scalable and interoperable semantic layers across data platforms (e.g., Lakehouse, Knowledge Graphs, Data Warehouses).
• Provide strategic guidance on data modeling techniques (3NF, dimensional, Data Vault, RDF/OWL) and drive adoption of best practices.
• Provide feedback that directly shapes the next generation of AI models.
• Knowledge of Machine Learning Operations (MLOps) workflows and tools for deploying, managing, and monitoring AI models in production.
• Extensive experience building big data pipelines.

Enterprise Architect – Data, AI

Salary

Job Level

Tech Stack

About the role

Requirements

Director of Enterprise Data

Senior Data Integration Engineer

Senior Data Operations Developer

Data Engineer

Data and Software Developer