Define and incorporate an AI first approach and strategy into the enterprise data architecture and vision
Establish the strategy and vision for how the entire data foundation will change with AI
Provide deep technical expertise in RAG models and semantic data search, semantic data models
Architect solutions for AI driven data engineering including unstructured data processing and AI driven dashboards & reports
Drive the operationalization of AI/ML and GenAI solutions, ensuring responsible AI practices and model governance. ### Full Stack & Platform Architecture
Apply full stack knowledge (backend, identity, front-end, Auth, and APIs) as the platform moves toward application centric delivery. - Design end-to-end data architectures, including ingestion, data processing, and consumption layers. - Platform engineering by developing and overseeing PoC’s
Establish architectural principles, standards, and best practices across data modeling, integration, and metadata management
Self Service & Data Enablement
Design an architecture that is completely self-oriented to support business self serve reporting and dashboarding. - Focus on enabling tools that make processes fully self-serve to reduce dependency on central IT teams. - Architect robust RBAC (Role-Based Access Control) layers and thoughtful metadata building. - Design governed data layers and semantic models for trusted access. ###
Developer Productivity & Governance
Enhance developer efficiency by utilizing AI tools to streamline the creation of data foundations, ETL pipelines, and model designs, while also improving the quality of AI-powered reporting and dashboards. - Define and enforce comprehensive data governance standards, including lineage, data quality, and observability, specifically tailored for AI-driven data products. - Architect a governed semantic layer (Knowledge Fabric) to ensure centralized definition of critical business metrics and consistent outputs for conversational analytics agents. - Drive AI-generated dashboards and reports to accelerate report generation, reduce backlog, and improve developer productivity. ## Experience you'll need:
Bachelor’s or Master’s degree in Computer Science, Engineering, Information Systems, or related field
10 - 15+ years in data engineering or platform architecture with a shift toward high level technical leadership
Strong experience designing and implementing modern data architectures and cloud data platforms. - Deep hands on expertise of full stack development that includes front end & back end technologies, DevOps infrastructure
Experience with Snowflake, Tableau or similar technologies, Expertise in AI/ML platforms and the engineering required to support GenAI at scale
Proficiency in Python, SQL, Spark, and advanced data modeling,
Experience with cloud platforms such as AWS, Azure, or GCP
Excellent stakeholder management skills to align technical data products with business needs. - Hands-on experience developing and deploying applications/Skills on AI-powered platforms such as Claude Code and Cowork. ## Join Us in Securing and Accelerating the World's AI Transformation
Rubrik (RBRK), the Security and AI Operations Company, leads at the intersection of data protection, cyber resilience, and enterprise AI acceleration. Rubrik Security Cloud delivers complete cyber resilience by securing, monitoring, and recovering data, identities, and workloads across clouds. Rubrik Agent Cloud accelerates trusted AI agent deployments at scale by monitoring and auditing agentic actions, enforcing real-time guardrails, fine-tuning for accuracy and undoing agentic mistakes. Linkedin | X (formerly Twitter) | Instagram | Rubrik.com
