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.
Skill Requirements
- 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 straightforward 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.
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📌 Technical Architect at HCLTech (Sydney)
🏢 HCL Technologies
📍 Sydney
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