• Architect and maintain a scalable, governed Snowflake AI Data Cloud environment including compute optimisation, warehouse sizing, and cost management.
• Design and implement ELT/ETL frameworks using Snowflake SQL, Snowpark Python, Dynamic Tables, Streams, Tasks, and Snowpipe for batch, streaming, CDC, and event-driven ingestion patterns.
• Build reusable, AI-ready data products with clear ownership, semantic context, data quality SLAs, and lineage tracking.
• Implement enterprise security controls: RBAC/ABAC, column- and row-level security, dynamic data masking, classification tags, and audit logging.
• Integrate Snowflake with upstream source systems, orchestration platforms (e.g., dbt, Apache Airflow), governance tools, and downstream analytics consumers.
• Design and build multi-step agentic AI workflows using Snowflake Cortex Agents, Cortex AI Functions, Cortex Search, and Cortex Analyst.
• Develop production-grade Retrieval-Augmented Generation (RAG) pipelines with vector search, semantic retrieval, and hybrid search capabilities over governed Snowflake datasets.
• Implement tool-use orchestration, prompt engineering, and evaluation frameworks (LLMOps) for reliable, auditable AI agent behaviour.
• Apply agentic automation to operational use cases including anomaly detection, data quality remediation, query diagnosis, documentation generation, and incident summarisation.
• Integrate AI agents with enterprise systems via MCP (Model Context Protocol) connectors, REST APIs, and event-driven messaging architectures.
• Define and enforce data modelling standards (Kimball dimensional, Data Vault 2.0) to support analytics, ML feature stores, and AI consumption layers.
• Build and maintain semantic data models that expose governed, business-ready datasets to Cortex Analyst and BI tools