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Rubrik is seeking an Architect for AI Data Platform & Engineering to lead the definition, governance, and evolution of enterprise data architecture with a focus on AI integration and self-service capabilities. This is a high-impact technical leadership role requiring end-to-end responsibility for modern, secure, and scalable data platform architecture.
Key responsibilities include:
**Strategy & AI Vision**: Define an AI-first approach to enterprise data architecture. Establish strategy for how the data foundation evolves with AI. Provide deep technical expertise in RAG models, semantic data search, and semantic data models. Architect solutions for AI-driven data engineering, including unstructured data processing and AI-powered dashboards. Drive operationalization of AI/ML and GenAI solutions with responsible AI practices and model governance.
**Full Stack & Platform Architecture**: Apply full-stack knowledge (backend, identity, front-end, auth, APIs) as the platform moves toward application-centric delivery. Design end-to-end data architectures covering ingestion, processing, and consumption layers. Develop and oversee proof-of-concept initiatives. Establish architectural principles, standards, and best practices for data modeling, integration, and metadata management.
**Self-Service & Data Enablement**: Design self-oriented architecture supporting business self-serve reporting and dashboarding. Enable tools that reduce dependency on central IT teams. Architect robust RBAC layers and thoughtful metadata building. Design governed data layers and semantic models for trusted access.
**Developer Productivity & Governance**: Enhance developer efficiency using AI tools to streamline data foundation and ETL pipeline creation. Define and enforce comprehensive data governance standards including lineage, quality, and observability for AI-driven data products. Architect a governed semantic layer (Knowledge Fabric) for centralized business metrics definition. Drive AI-generated dashboards to accelerate report generation.
Required qualifications: Bachelor's or Master's in Computer Science, Engineering, or related field. 10-15+ years in data engineering or platform architecture with shift toward high-level technical leadership. Strong experience designing modern data architectures and cloud data platforms. Deep hands-on expertise in full-stack development including front-end, back-end, and DevOps infrastructure. Experience with Snowflake, Tableau, or similar technologies. Expertise in AI/ML platforms and engineering for GenAI at scale. Proficiency in Python, SQL, Spark, and advanced data modeling. Experience with AWS, Azure, or GCP. Excellent stakeholder management skills. Hands-on experience with AI-powered platforms.
