AVP Cloud Data Analytics Architecture

GM Financial•Irving, TX

1d•Hybrid

About The Position

Why GM Financial Technology? Innovation isn’t just a talking point at GM Financial, it’s how we operate. From generative AI and cloud-native technologies to peer-led learning and hackathons, our tech teams are building real solutions that make a difference. We’re committed to AI-powered transformation, using advanced machine learning and automation to help us reimagine customer interactions and modernize operations, positioning GM Financial as a leader in digital innovation within a dynamic industry. Join us and discover a workplace where your ideas matter, your development is prioritized, and you can truly make a global impact. This position will be posted until filled. About the role: The AVP Cloud Data Analytics Architecture will lead the cloud data architecture team and scale the Data & Analytics organization globally. As an experienced cloud data architect, this role will partner with business stakeholders to capture data, analytics, AI/ML, and GenAI requirements; design, develop, and deploy Enterprise Cloud Data and AI solutions; and integrate data from disparate sources across cloud, hybrid, and multi-cloud environments, deploy compliant infrastructure and support cloud resources (SRE). They will bring hands-on expertise in Azure (Data & AI), Databricks (Delta Lake, MLflow, Model Registry, Feature Store), APIs, microservices, and event-driven architectures. The AVP will ensure the cloud, data, machine learning, and AI platforms are scalable, secure, cost-optimized (FinOps), and compliant to meet future growth and business domain requirements. With a passion for building agile teams, this leader will drive planning and execution while collaborating across cross-functional teams to deliver mission-critical outcomes. The AVP will build 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.

Requirements

7–10 years building enterprise-scale cloud data architecture and applications to support ML/AI and analytics (required).
7–10 years in cloud application development solutions (PaaS, SaaS, IaaS, Serverless, Data Orchestration, API Management) (required).
7–10 years with scalable architectures using Azure App Service, API Management, serverless, container orchestration, microservice frameworks (required).
7–10 years with DevOps and CI/CD toolchains (Azure DevOps, GitHub) (required).
7–10 years management or leadership experience (required).
High School Diploma or equivalent (required).
Bachelor’s Degree in a related field or equivalent work experience (required).
Ability to use AI tools (e.g., Microsoft Copilot) to support daily work
Experience integrating AI into workflows to improve efficiency or insights
Understanding of responsible AI use, including ethics and data protection

Nice To Haves

3+ years delivering production ML/AI solutions (preferred), including Databricks ML and Azure Machine Learning.
Master’s Degree in a related field (preferred).
Microsoft Certified: Azure Solutions Architect Expert, Azure Data Engineer Associate, Azure AI Engineer Associate
Databricks: Certified Data Engineer Professional / Machine Learning Professional
Working effectively within an AI enabled environment:
Experience evaluating AI outputs for accuracy, compliance, and bias
Familiarity with AI assisted research, summarization, and content generation

Responsibilities

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 blockers to accelerate AI and data initiatives.