AI Data Architect

Role Overview

Enterprise Data Architecture

• Design enterprise-scale data, big data, analytics, and AI platforms.
• Architect data lakes, lakehouses, data warehouses, data marts, and big data environments.
• Design architectures supporting structured, semi-structured, and unstructured data.
• Establish data platform standards, reference architectures, and best practices.
• Design scalable batch, streaming, and real-time data processing ecosystems.
• Define patterns for data ingestion, integration, transformation, orchestration, and delivery.
• Create conceptual, logical, and physical data models.

AI and Data Governance

• Design and govern architectures supporting AI, machine learning, advanced analytics, and generative AI workloads.
• Ensure enterprise data is discoverable, trusted, governed, and consumable for AI initiatives.
• Design metadata, lineage, cataloging, and semantic layer capabilities.
• Establish architecture standards for RAG, knowledge graphs, vector stores, AI agents, and MLOps platforms.
• Define controls to improve data quality and reliability for AI training and inference.
• Collaborate with data science and AI teams on AI-ready data platforms.
• Own master data management, reference data, golden records, and authoritative sources.
• Strengthen stewardship, ownership, classification, retention, and policy enforcement.

Architecture Governance

• Own architecture standards, reference architectures, design reviews, and architecture decision records.
• Define trusted sources for each data domain through single-source-of-truth practices.

Required Experience

• At least 10 years of experience in data architecture, big data architecture, or enterprise information management.
• Strong hands-on experience designing and implementing enterprise on-premises data platforms.
• Proven experience designing large-scale data warehouses, data lakes, and lakehouse platforms.
• Experience designing big data environments for high-volume and high-velocity workloads.
• Experience establishing enterprise data governance and data quality frameworks.
• Experience supporting AI, machine learning, and advanced analytics initiatives through enterprise data architecture.
• Experience working with distributed processing and storage platforms.