AI Data Architect

Data Architect - AI Job Summary We are seeking an experienced Data Architect to design, govern, and optimize enterprise Data, Big Data, Analytics, and AI platforms. Enterprise Data & Big 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 highly scalable batch, streaming, and real-time data processing ecosystems. • Define enterprise patterns for data ingestion, integration, transformation, orchestration, and delivery. • Responsible for creating conceptual, logical and physical data models. AI-Ready Data Foundation and Data governance • Design and govern data architectures that support 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 required for AI adoption. • Establish architecture standards supporting RAG, Knowledge Graphs, Vector Stores, AI Agents, and MLOps platforms. • Define processes and controls to improve data quality and data reliability for AI model training and inference. • Collaborate with Data Science and AI teams to ensure data platforms meet AI readiness requirements • Ownership of MDM and reference data, including golden records, master data domains and authoritative sources. • Make SSOT a clear responsibility, especially identifying which systems should be treated as the trusted source for each data domain. • Strengthen the role around data governance, including stewardship, ownership, classification, retention and policy enforcement. • Add ownership of architecture standards, reference architectures, design reviews and architecture decision records. Required Experience • 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 handling 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. Show more Show less

Required Experience: 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 handling 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.

Data Architect - AI: 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 highly scalable batch, streaming, and real-time data processing ecosystems. Define enterprise patterns for data ingestion, integration, transformation, orchestration, and delivery. Responsible for creating conceptual, logical and physical data models. Design and govern data architectures that support 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 required for AI adoption. Establish architecture standards supporting RAG, Knowledge Graphs, Vector Stores, AI Agents, and MLOps platforms. Define processes and controls to improve data quality and data reliability for AI model training and inference. Collaborate with Data Science and AI teams to ensure data platforms meet AI readiness requirements. Ownership of MDM and reference data, including golden records, master data domains and authoritative sources. Make SSOT a clear responsibility, especially identifying which systems should be treated as the trusted source for each data domain. Strengthen the role around data governance, including stewardship, ownership, classification, retention and policy enforcement. Add ownership of architecture standards, reference architectures, design reviews and architecture decision records.