Job Description:
• 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.
Requirements:
• BS/MS in Computer Science or equivalent professional experience.
• 14+ years of engineering experience, including significant experience designing and delivering large-scale data platforms and distributed systems.
• Proven leadership in data and/or analytics architecture, engineering standards, and cross-functional collaboration across product, engineering, and infrastructure teams.
• Recognized expertise in multiple data domains, such as data warehousing/lakehousing, data modeling, streaming/event-based architectures, and analytics/BI enablement.
• Deep experience with modern data platform technologies, including hands-on architectural experience with Snowflake (or a closely comparable cloud data warehouse) as well as SQL Server/Azure SQL, cloud storage, streaming platforms, and ETL/ELT tooling.
• Strong understanding of AI/ML data needs, including feature engineering, model training/serving data flows, vector stores, and observability for data-driven and AI-powered products; experience designing data architectures that reliably feed ML platforms, feature stores, or model-serving layers.
• Demonstrated success leading modernization and decomposition efforts (e.g., monolithic reporting stacks to domain-aligned, Snowflake-centric cloud-native data platforms; batch pipelines to streaming/event-driven patterns).
• Mastery of system and data design, including tradeoff analysis for scalability, reliability, performance, cost (including Snowflake cost optimization), and regulatory requirements.
• Track record of influencing company-wide technical strategy through data and AI architecture decisions, with measurable impact on product velocity and quality.
• Excellent communication and storytelling skills, capable of explaining complex data and AI concepts to both technical and non-technical stakeholders, including executives.
• Strong coaching and mentorship skills, with experience developing senior engineers and architects in data and AI domains.
Benefits:
• Comprehensive medical benefits, 100% paid for employee
• 401k + matching
• Equity Option Grant
• Unlimited PTO + Company holidays
• Wellness initiatives