Job Title: Data Architect - Agentic AI & Databricks
Location: Boston, MA
FTE / Contract
About the Role
We are looking for a Data Architect with deep expertise in the Databricks ecosystem and Databricks architecture to design and deliver enterprise-grade Agentic AI and data platform solutions across AWS and Databricks.
The role combines modern data architecture with Generative AI and Agentic AI capabilities, including Databricks Genie, Genie Ontology, Genie AI Gateway, AgentBricks, Vector Search, RAG, agent orchestration, semantic layers and knowledge graphs. The ideal candidate will be able to design the underlying Lakehouse, warehouse, data modeling, governance and semantic foundations required to provide AI agents with reliable, governed and business-aware enterprise context.
Responsibilities
- Architect and deliver enterprise data and AI solutions across the Databricks and AWS ecosystems.
- Design Databricks platform architecture including workspace strategy, data architecture, compute, security, governance and production deployment patterns.
- Design and implement conversational analytics solutions using Databricks Genie, including Genie Ontology and Genie AI Gateway.
- Build and orchestrate AI agents and agentic workflows across AWS and Databricks.
- Design agent workflows involving tool calling, retrieval, enterprise data access and downstream APIs/services.
- Build, validate and evaluate AI agents using structured evaluation approaches before production deployment.
- Implement enterprise retrieval and grounding solutions using Databricks Vector Search and other vector search technologies.
- Design RAG and semantic retrieval architectures that provide AI agents with accurate and governed enterprise context.
- Define ontology, knowledge graph and semantic layer foundations to represent business entities, relationships, terminology and metrics.
- Architect scalable Data Warehouse and Lakehouse platforms across Databricks and AWS.
- Design data models across Bronze, Silver and Gold/layered architectures and establish appropriate analytical and semantic models.
- Establish data cataloging, metadata management and governance practices.
- Partner with governance teams to maintain business glossaries, data definitions, lineage and ownership.
- Collaborate with engineering, data science, AI/ML, governance and business teams to translate enterprise requirements into scalable architectures.
- Ensure AI and data solutions meet requirements for security, governance, reliability, scalability, observability and production readiness.
Required Skills
- Deep expertise in the Databricks ecosystem and Databricks architecture.
- Hands-on experience with Databricks Lakehouse architecture and data platform design.
- Experience with Databricks Genie and conversational analytics.
- Knowledge of Genie Ontology and Genie AI Gateway.
- Hands-on understanding of Agentic AI, AI agent development and agent orchestration.
- Experience building, validating and evaluating AI agents across AWS and/or Databricks.
- Knowledge of AgentBricks or comparable agent-building technologies.
- Experience with Databricks Vector Search or other enterprise vector search technologies.
- Strong understanding of RAG, semantic search, embeddings and AI grounding.
- Strong Data Warehouse and Lakehouse architecture experience.
- Strong understanding of data modeling and enterprise data architecture.
- Knowledge of ontology, semantic layers and knowledge graphs.
- Experience with data cataloging, metadata management and business glossaries.
- Strong AWS cloud architecture experience.
Preferred Skills
- Experience with Atlan or other enterprise data catalog platforms.
- Experience with Unity Catalog and Databricks governance.
- Experience with Collibra, Alation, Informatica or Microsoft Purview.
- Experience with Amazon Bedrock / Bedrock Agents.
- Experience with knowledge graph technologies such as Neo4j or Amazon Neptune.
- Experience with enterprise RAG and Graph RAG architectures.
- Experience with LLM/Agent evaluation frameworks and LLM-as-a-Judge approaches.
- Banking or Financial Services domain experience.
- Experience working with banking assets, lending, mortgage, credit or other financial products and their associated data.