Data & Analytics Engineer - Consultant
The consultant will design and deploy conversational AI solutions on Snowflake using Cortex and Snowpark, and build Streamlit applications for AI‑powered analytics. They will also create robust ETL/ELT pipelines, manage semantic models, and ensure governed, AI‑ready data products across bronze, silver, and gold layers.
Skills required
About this role
Snowflake Cortex AI Engineer: Consultant
4+ years Snowflake experience with strong grasp of architecture (compute–storage separation, micro-partitions, virtual warehouses, RBAC) and features (Snow pipe, Streams/Tasks, Dynamic Tables).
Design, develop, and deploy Conversational AI solutions on Snowflake using Cortex Analyst, Cortex Agents, Cortex Search, and Snowpark, enabling natural language interaction with enterprise data.
Build and maintain Streamlit applications on Snowflake to deliver secure, scalable, and user-friendly AI-powered analytics and chatbot experiences.
Develop robust ETL/ELT data pipelines using Snowflake, Python, SQL, and orchestration tools to ingest, transform, and curate data across Bronze, Silver , and Gold layers.
Create and manage semantic models, business metrics, governed data products, and AI-ready data models to improve the accuracy and reliability of AI-generated insights.
Implement best practices for performance optimization, security, RBAC, data governance, monitoring, CI/CD, Git integration, and automated deployments within Snowflake environments.
Collaborate with business stakeholders, data engineers, and analytics teams to translate business requirements into scalable AI, analytics, and data engineering solutions, while troubleshooting and optimizing production workloads.
Responsibilities
Snowflake Cortex AI Engineer: Consultant
4+ years Snowflake experience with strong grasp of architecture (compute–storage separation, micro-partitions, virtual warehouses, RBAC) and features (Snow pipe, Streams/Tasks, Dynamic Tables).
Design, develop, and deploy Conversational AI solutions on Snowflake using Cortex Analyst, Cortex Agents, Cortex Search, and Snowpark, enabling natural language interaction with enterprise data.
Build and maintain Streamlit applications on Snowflake to deliver secure, scalable, and user-friendly AI-powered analytics and chatbot experiences.
Develop robust ETL/ELT data pipelines using Snowflake, Python, SQL, and orchestration tools to ingest, transform, and curate data across Bronze, Silver , and Gold layers.
Create and manage semantic models, business metrics, governed data products, and AI-ready data models to improve the accuracy and reliability of AI-generated insights.
Implement best practices for performance optimization, security, RBAC, data governance, monitoring, CI/CD, Git integration, and automated deployments within Snowflake environments.
Collaborate with business stakeholders, data engineers, and analytics teams to translate business requirements into scalable AI, analytics, and data engineering solutions, while troubleshooting and optimizing production workloads.
Qualifications
Snowflake Cortex AI Engineer: Consultant
4+ years Snowflake experience with strong grasp of architecture (compute–storage separation, micro-partitions, virtual warehouses, RBAC) and features (Snow pipe, Streams/Tasks, Dynamic Tables).
Design, develop, and deploy Conversational AI solutions on Snowflake using Cortex Analyst, Cortex Agents, Cortex Search, and Snowpark, enabling natural language interaction with enterprise data.
Build and maintain Streamlit applications on Snowflake to deliver secure, scalable, and user-friendly AI-powered analytics and chatbot experiences.
Develop robust ETL/ELT data pipelines using Snowflake, Python, SQL, and orchestration tools to ingest, transform, and curate data across Bronze, Silver , and Gold layers.
Create and manage semantic models, business metrics, governed data products, and AI-ready data models to improve the accuracy and reliability of AI-generated insights.
Implement best practices for performance optimization, security, RBAC, data governance, monitoring, CI/CD, Git integration, and automated deployments within Snowflake environments.
Collaborate with business stakeholders, data engineers, and analytics teams to translate business requirements into scalable AI, analytics, and data engineering solutions, while troubleshooting and optimizing production workloads.
