• Define and enable data architecture and engineering standards across Product Lines, ensuring consistent patterns for modeling, storage, access, governance, and responsible AI/ML usage. • Lead data-focused architecture reviews and technical governance for new products, platform initiatives, and AI/ML solutions, with particular emphasis on scalable, secure use of Snowflake and related cloud data services. • Own the reference architectures for enterprise data platforms, including Snowflake-based warehouses/lakehouses, streaming/real-time data, semantic models, and analytics/BI consumption patterns used across product teams. • Partner with Product, Engineering, Infrastructure, and Security to design data platforms that support analytics, reporting, operational workloads, and AI/ML use cases, ensuring clear data contracts and safe pathways for AI feature development. • Drive modernization of legacy schemas, ETL/ELT pipelines, and reporting stacks into scalable, cloud-native data and AI platforms with clear service and data domain boundaries, leveraging Snowflake and modern orchestration tooling where appropriate. • Define and promote patterns for data contracts, event-driven architecture, and CDC/streaming feeds that enable reliable, low-latency data sharing across systems and make high-quality data continuously available for AI/ML workloads. • Establish standards and best practices for data quality, lineage, observability, cataloging, and governance (including privacy, security, and compliance requirements) so AI/ML features are built on trustworthy, well-understood datasets. • Partner with AI and engineering teams to design data and feature platforms that power modern AI workloads (e.g., feature stores, vector search, retrieval-augmented generation, and model-serving integrations), including how these are realized on Snowflake and adjacent services. • Anticipate long-term risks in data growth, performance, reliability, and cost (including Snowflake usage and storage/compute patterns), and design solutions that prevent systemic issues across products and environments. • Mentor architects, principal engineers, and data engineers across teams, raising organizational maturity in data and AI architecture and accelerating adoption of shared patterns for Snowflake, data contracts, and AI/ML enablement. • Operate as a trusted technical advisor to executives and senior leaders, translating data and AI architecture decisions into clear business value, risk tradeoffs, and roadmap implications. • Represent the company in data and AI technical communities, helping position Restaurant365 as a leading data-driven and AI-enabled SaaS platform.