AVP Cloud Data Analytics Architecture

AVP Cloud Data Analytics Architecture – GM Financial

Innovation is not just a talking point at GM Financial. It shapes how we operate – from generative AI and cloud‑native technologies to peer‑led learning and hackathons. Our tech teams build real solutions that transform customer interactions and modernize operations, positioning GM Financial as a digital innovation leader.

The AVP Cloud Data Analytics Architecture will lead the cloud data architecture team and scale the Data & Analytics organization globally. Partnering with business stakeholders, the role captures data, analytics, AI/ML, and GenAI requirements; designs, develops, and deploys enterprise cloud data and AI solutions; integrates data from disparate sources across cloud, hybrid, and multi‑cloud environments; and supports compliant infrastructure and cloud resources (SRE).

Hands‑on expertise in Azure (Data & AI), Databricks (Delta Lake, MLflow, Model Registry, Feature Store), APIs, microservices, and event‑driven architectures is required. The AVP ensures the cloud, data, machine learning, and AI platforms are scalable, secure, cost‑optimized (Fin Ops), and compliant to meet future growth and business domain requirements.

With a passion for building agile teams, this leader will drive planning and execution, collaborating across cross‑functional teams to deliver mission‑critical outcomes. The AVP builds strong partnerships with cloud data architects, cloud platform teams, engineering teams, and vendors to scale global data and AI architecture and capabilities across the enterprise.

• 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.
• 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.

• 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…