Snowflake Data Engineer

Job Description

We are seeking a highly skilled Snowflake Data Engineer with deep expertise in Snowflake's modern AI and data platform capabilities. The ideal candidate goes beyond traditional data engineering — they are proficient in Snowflake Cortex AI, building intelligent agents, developing Streamlit-based data applications, and leveraging Snowpark for advanced analytics. This role is central to our healthcare data modernization strategy and requires someone who can architect, build, and scale next-generation data and AI solutions on Snowflake.

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Responsibilities:

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Snowflake AI & Cortex (Core Focus)

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• Design and deploy AI-powered solutions using Snowflake Cortex, including LLM-based functions (COMPLETE, SUMMARIZE, SENTIMENT, TRANSLATE, CLASSIFY) for healthcare data use cases.

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• Build and orchestrate multi-step AI agents using Snowflake Cortex Agents and Cortex Analyst to automate data workflows and enable natural language querying of structured healthcare datasets.

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• Leverage Cortex Search for semantic and hybrid search capabilities across unstructured clinical and operational data.

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• Implement RAG (Retrieval-Augmented Generation) pipelines using Snowflake's native vector embeddings and VECTOR data type for domain-specific LLM grounding.

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• Use Arctic Embed and other Snowflake-native embedding models to build scalable, cost-effective AI features within the Snowflake perimeter.

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Streamlit & Application Development

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• Build interactive, production-grade data applications and internal tools using Streamlit in Snowflake (SiS) for business stakeholders and data consumers.

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• Develop self-service analytics dashboards and AI-assisted exploration tools embedded directly in the Snowflake environment.

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• Create Streamlit-based front-ends for Cortex-powered features — enabling clinical users and analysts to interact with AI outputs without leaving the Snowflake ecosystem.

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Snowpark & Data Engineering

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• Develop complex data transformations, feature engineering pipelines, and ML preprocessing workflows using Snowpark for Python.

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• Build and manage scalable ELT pipelines leveraging Snowflake Streams, Tasks, Dynamic Tables, and Snowpipe for real-time and batch ingestion.

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• Optimize Snowflake performance through micro-partition pruning, clustering keys, materialized views, search optimization, and warehouse right-sizing.

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• Design and implement Data Vault or Medallion architecture patterns for healthcare data platforms.

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• Integrate Snowflake with upstream healthcare data sources and downstream BI platforms.

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Data Governance & Security

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• Implement Snowflake-native governance including row-level security, column masking policies, object tagging, and data classification.

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• Ensure HIPAA compliance and PHI data handling best practices across all Snowflake data assets.

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• Manage data lineage, cataloging, and access control in alignment with organizational governance frameworks.

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Collaboration & Platform Ownership

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• Partner with data scientists, ML engineers, clinical analysts, and business stakeholders to translate requirements into robust Snowflake-native solutions.

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• Act as a Snowflake subject matter expert and internal champion — driving best practices, onboarding teams, and evangelizing new platform capabilities.

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• Participate in platform roadmap planning and contribute to Snowflake feature adoption strategy.

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Qualifications:

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• 5+ years of data engineering experience, with at least 3+ years of hands-on Snowflake expertise.

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• Proven experience with Snowflake Cortex AI features: LLM functions, Cortex Analyst, Cortex Search, and AI agent orchestration.

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• Hands-on experience building Streamlit in Snowflake applications deployed in production environments.

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• Proficiency in Snowpark for Python — including custom UDFs/UDTFs, stored procedures, and DataFrame API usage.

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• Strong SQL skills with deep understanding of Snowflake's execution engine, query profiling, and optimization techniques.

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• Experience with Snowflake data sharing, Marketplace, and cross-cloud/cross-region replication.

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• Familiarity with vector search, embeddings, and RAG architecture patterns within or integrated into Snowflake.

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• Good to have experience with cloud platforms: Azure, and GCP — and connecting Snowflake to cloud-native services.

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• Strong understanding of data modeling for analytics (star schema, Data Vault, Medallion/Lakehouse patterns).

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• Knowledge of Medicare Stars Patient Safety is a strong plus.

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