Technical Architect

Job Summary

• Architectural Leadership: Design and govern end-to-end data architectures on the Databricks Data Intelligence Platform, ensuring seamless integration with existing insurance ecosystems.
• AI Innovation: Lead the design of emerging AI workflows, specifically focusing on Agentic AI (multi-agent systems), LLM orchestration (LangChain/LlamaIndex), and RAG patterns tailored for insurance policy and regulatory documents.
• Databricks FinOps & Performance: Own the platform’s efficiency. Implement cluster policies, Photon engine optimisations, and cost-attribution models to ensure high performance with a focus on ROI.
• Data Engineering Excellence: Standardise data engineering templates (Medallion architecture) and implement robust data modelling (Star/Snowflake, Vault 2.0) to handle structured and unstructured insurance data.
• Domain Alignment: Translate complex insurance concepts (Premiums, Claims, Actuarial models) into scalable technical schemas and AI logic.
• Governance & Security: Ensure all designs comply with industry regulations using Databricks Unity Catalog.

Key Responsibilities

• Experience: 8+ years in Data Engineering/Architecture, with at least 3+ years of hands-on leadership within the Databricks ecosystem.
• Core Tech: Expert-level knowledge of the Spark framework (PySpark/Scala), Delta Lake, and Databricks SQL.
• AI/ML Expertise: Proven experience building and deploying AI solutions. Deep understanding of the Agentic AI landscape-moving beyond simple chatbots to autonomous, goal-oriented agents.
• FinOps Mastery: Specific experience in Databricks cost management, instance selection, and performance tuning (Z-Ordering, Liquid Clustering).
• Data Management: Strong background in data modelling and the creation of reusable ETL/ELT templates and frameworks.
• Insurance Domain: Prior experience in the Insurance or FinServ sector is highly preferred; you must understand the nuances of policy-centric data.

Skill Requirements

Other Requirements