What are the responsibilities and job description for the Snowflake Data Architect-Snowflake Data Modeling position at Jobs via Dice?
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Roles & Responsibilities
- Client is not sponsoring any VISA************
- Snowflake expertise: Warehouses, databases, roles, RBAC, SCIM, MFA.
- Data Engineering: ELT/ETL tools (dbt, Talend), orchestration (Airflow).
- Cloud Platforms: AWS, Azure, or Google Cloud Platform with Snowflake integration.
- Programming: SQL, Python; familiarity with ML frameworks.
- Security & Compliance: Data masking, encryption, audit processes
- Strong experience with LLMs (OpenAI, Anthropic, Hugging Face, LangChain).
- Proficiency in Python and modern AI frameworks.
- Familiarity with vector databases, prompt engineering, and AI best practices.
- 5 years of product-focused engineering experience.
- Knowledge of cloud deployment and scaling AI systems.
- Strong SQL and Python skills.
- Hands-on experience with dbt and Snowflake.
- Familiarity with cloud platforms (AWS ).
- Knowledge of CI/CD, DevOps practices, and data orchestration tools (Airflow, Prefect).
- Ability to create lineage graphs, documentation, and validation frameworks
Roles & Responsibilities
- Design and manage data pipelines using dbt, Airflow, and CI/CD frameworks.
- Implement Snowpipe for continuous ingestion, Streams & Tasks for real-time processing.
- Enable AI/ML integration: Support predictive analytics and generative AI use cases.
- Leverage Snowflake Cortex and Copilot for LLM-based applications.
- Ensure data governance, RBAC, and security compliance across Snowflake environments.
- Optimize performance and implement Time Travel, Zero Copy Cloning, and Secure Data Sharing
- Build production-ready AI applications and LLM-powered features.
- Collaborate with AI Data teams to develop agentic AI workflows.
- Experiment with open-source models and translate prototypes into production systems.
- Implement RAG pipelines, fine-tuning, and observability for AI models.
- Design and deploy secure, scalable, and highly available architectures on AWS.
- Select appropriate AWS services for application design and deployment.
- Implement cost-control strategies and disaster recovery plans.
- Collaborate with teams to integrate systems and ensure compliance.
- Develop Infrastructure as Code (IaC) using Terraform or CloudFormation
- Design, build, and maintain data pipelines using dbt for analytics and operational use cases.
- Implement standards for data quality, consistency, and reliability. Optimize query performance and manage compute costs.
- Collaborate with analysts and stakeholders to understand data requirements.
- Build automation into workflows and ensure compliance with governance policies.