Senior Data Architect

Data Architect — Databricks & Agentic AI

ROLE OVERVIEW

We are looking for a Data Architect who is well versed in the Databricks ecosystem and Databricks architecture to design and deliver agentic AI and data platform solutions on AWS and Databricks. The role spans conversational analytics with Databricks Genie, agent building and orchestration, and the warehouse, lakehouse, and semantic foundations that ground these solutions in governed business context.

KEY RESPONSIBILITIES

● Architect and deliver data and AI solutions across the Databricks ecosystem, applying deep knowledge of Databricks architecture, workspace design, and platform governance.

● Design and implement conversational analytics using Databricks Genie, including Genie Ontology and the Genie AI Gateway.

● Build and orchestrate AI agents in the AWS and Databricks ecosystems, then validate them and run structured evaluations before production.

● Implement retrieval and grounding using Databricks Vector Search and general vector search techniques.

● Define ontology, knowledge graph, and semantic layer foundations that give agents reliable, governed business context.

● Architect data warehouse and lakehouse solutions with sound data modelling across layers.

● Establish data cataloging practices and maintain the business glossary with governance teams.

WHAT WE'RE LOOKING FOR

MUST-HAVE SKILLS

Databricks Ecosystem — Deep expertise in the Databricks ecosystem and architecture, from design through production.

Databricks Genie — Working knowledge of Databricks Genie, Genie Ontology, and the Genie AI Gateway.

Agentic AI Implementation — Understanding of agentic implementation, agent building, and orchestration, with hands-on ability to build, validate, and evaluate agents on AWS and Databricks.

AgentBricks & Vector Search — Knowledge of AgentBricks and Databricks Vector Search, or general vector search.

Data Warehouse & Lakehouse — Rich data warehouse and lakehouse experience, plus data modelling fundamentals.

Semantic Foundations — Fundamental knowledge of ontology, knowledge graphs, and semantic layers.

Data Cataloging & Glossary — Knowledge of data cataloging tools and business glossaries.

GOOD-TO-HAVE SKILLS

Cataloging Tools such as Atlan — Familiarity with commercial cataloging platforms, particularly Atlan, is a bonus.

Banking Domain Knowledge — Understanding of banking domains and banking data landscapes. Banking Assets Domain — Exposure to the banking assets domain, including lending and asset products and their data.