• Own analytics data architecture, including data transform, modeling, and serving layers. • Partner with Data Governance and IT Architecture to define and enforce data modeling standards (e.g., dimensional, semantic, or metric layers) to support self-service analytics and consistent metrics. • Lead architectural decisions around cloud data warehouses and ML orchestration frameworks. • Partner with analytics and business teams to ensure data platform is usable, trusted, and performant, not just technically elegant. • Establish technical best practices for data quality, lineage, metadata, and governance in collaboration with data governance team. • Design and operate the ML/AI platform supporting the full model lifecycle (experimentation, training, validation, deployment, and monitoring) in partnership with data science and engineering teams. • Determine the need and design of feature engineering stores to reduce friction from research to production. • Design and develop framework for model versioning & end-to-end reproducibility • Build and operate a CI/CD for ML/AI solution that enables model deployment & monitoring into production systems at scale. • Collaborate with model governance, cyber security & architecture, privacy, cloud architecture and other stakeholders to maintain enterprise wide MLOps standards • Set the technical vision and roadmap for analytics and ML platforms aligned to business strategy. • Make clear trade-offs between build vs. buy, speed vs. scale, and experimentation vs. operational rigor. • Lead architecture reviews and provide technical guidance on complex initiatives within the data and ML platforms. • Stay current on evolving data and ML platform technologies and assess relevance pragmatically. • Lead and mentor senior data engineers, analytics engineers, and ML platform engineers. • Establish clear technical standards, documentation, and operational practices for the data and ML platforms. • Collaborate with product, engineering, analytics, security, and infrastructure teams to ensure platform alignment and reliability. • Influence without authority across teams that depend on the data and ML platform.