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We are seeking a Data and AI Architect to design shared data architecture for Agentic AI solutions on Databricks. You will shape secure ingestion, modeling and governance for IoT and time-series data and enable RAG retrieval patterns using Delta Lake, Unity Catalog, Spark and Vector Search. You simplify complexity and guide teams toward scalable, reusable designs.
You will define the shared data architecture for Agentic AI solutions built on Databricks. Your focus is creating a secure, scalable and reusable foundation for ingestion, modeling, governance and retrieval patterns that enable AI-ready data products.
You bring a pragmatic systems mindset, and you can translate complex data and AI requirements into clear architecture decisions. You collaborate across engineering, product and security teams, challenge assumptions constructively and manage risks early.
- Design shared data and AI architecture on Databricks across Agentic AI solutions
- Define data modeling standards for IoT and time-series domains aligned to analytics and RAG needs
- Architect ingestion and processing pipelines using Spark and Delta Lake
- Establish governance, lineage and access controls with Unity Catalog
- Define secure data access patterns for internal teams and downstream consumption
- Design RAG retrieval patterns including indexing strategy and datasets for Vector Search
- Produce architecture decision records, reference designs and reusable templates
- Partner with stakeholders to align scope, manage risks and drive implementation quality
- Databricks architecture experience in production environments
- Data modeling expertise including event, time-series, dimensional and canonical modeling patterns
- Ingestion pipeline experience across batch and streaming patterns
- IoT and time-series data experience including scale, retention and query optimization
- Delta Lake expertise including table design, performance tuning and lifecycle management
- Databricks Unity Catalog experience covering governance, lineage and fine-grained access control
- Apache Spark expertise for large-scale processing and optimization
- Data governance and security experience including policy design and secure-by-default architecture
- Experience implementing Vector Search and managing embedding lifecycle
- Familiarity with retrieval quality, evaluation signals and RAG tuning considerations
- Experience enabling shared data products across multiple teams