AVP Cloud Data Analytics Architecture

What makes you an ideal candidate: Strategy & Leadership Architect the data and analytics platform including AI/ML and GenAI capabilities to support the Company’s vision, goals, and strategies. Develop cloud architecture solutions for data, machine learning, artificial intelligence (including LLMs), and analytics leveraging Azure, Informatica, and Databricks including cloud infrastructure. Translate broad strategies into AI-enabled data architecture blueprints and roadmaps, aligning to strategic objectives and measurable business outcomes. Collaborate with Data Leadership to define cloud data & AI architecture, Digital Transformation, and Data & Analytics priorities and goals. Partner with the VP Cloud Data Analytics Architecture on department performance and accountability for business results.

Architecture & Delivery Architect the end-to-end flow of data and AI features from transactional systems and master data through curation layers (bronze/silver/gold) into cloud data platforms (ADLS, Delta Lake) and consuming applications/services. Design RAG (Retrieval-Augmented Generation) and LLM reference architectures on Azure using Databricks, Azure Machine Learning, Azure Cognitive Search (vector), and Azure OpenAI Service where appropriate. Architect and monitor data and model pipelines across Event Hubs/Service Bus, APIs/microservices, and streaming frameworks to support real-time analytics and AI inference. Establish AI-ready data models, semantic layers, and feature engineering standards to fuel ML and GenAI workloads. Interact with software vendors, data and service providers supporting AI/data architecture and integration initiatives in the cloud. Define and report release needs for product/architecture with respect to business objectives, security, data dependency, compliance, and timeliness.

Collaboration & Enablement Collaborate with business and technical teams to develop end-to-end enterprise solutions for data, analytics, machine learning, and artificial intelligence in the cloud. Coach, mentor, and train cloud data architecture team members on AI platform patterns, MLOps/LLMOps, Databricks ML, Azure ML, and secure development practices. Assist leadership in annual planning, budgeting, and capacity planning for AI & data platform investments and managed services. Champion an environment of trust, continuous improvement, innovation, quality outcomes, and self-development. Develop relationships with key business and technical decision makers; drive long-term cloud data & AI adoption; enable internal advocacy and best-practices sharing. Share insights and best practices; proactively remove architectural blockers to accelerate AI and data initiatives.