Snowflake Data Engineer

EXL•Pittsburgh, PA

About The Position

We are seeking a highly skilled Snowflake Data Engineer to support healthcare data modernization by architecting, building, and scaling next-generation data and AI solutions on Snowflake. The role focuses on Snowflake Cortex AI, intelligent agents, Streamlit applications, and Snowpark-based advanced analytics, as well as secure, scalable data engineering and platform ownership. The engineer will develop AI-powered solutions and multi-step agents for healthcare data, build production-grade Streamlit applications, and create Snowpark transformations and data pipelines. The position also includes optimizing Snowflake performance, integrating healthcare data sources and BI platforms, and implementing governance and security practices that protect PHI and support HIPAA compliance. The role partners with data scientists, ML engineers, clinical analysts, and business stakeholders to turn requirements into Snowflake-native solutions. It also serves as an internal Snowflake subject matter expert, contributing to platform planning, team onboarding, best practices, and adoption of new capabilities.

Requirements

At least 5 years of data engineering experience.
At least 3 years of hands-on Snowflake experience.
Proven experience with Snowflake Cortex AI features, including LLM functions, Cortex Analyst, Cortex Search, and AI agent orchestration.
Hands-on experience building Streamlit in Snowflake applications deployed in production environments.
Proficiency in Snowpark for Python, including custom UDFs/UDTFs, stored procedures, and DataFrame API usage.
Strong SQL skills and a deep understanding of Snowflake's execution engine, query profiling, and optimization techniques.
Experience with Snowflake data sharing, Marketplace, and cross-cloud/cross-region replication.
Familiarity with vector search, embeddings, and RAG architecture patterns within or integrated with Snowflake.
Strong understanding of data modeling for analytics, including star schema, Data Vault, and Medallion/Lakehouse patterns.

Nice To Haves

Experience with Azure and GCP cloud platforms and connecting Snowflake to cloud-native services.
Knowledge of Medicare Stars Patient Safety.

Responsibilities

Design and deploy AI-powered healthcare solutions using Snowflake Cortex LLM functions, including COMPLETE, SUMMARIZE, SENTIMENT, TRANSLATE, and CLASSIFY.
Build and orchestrate multi-step AI agents with Snowflake Cortex Agents and Cortex Analyst to automate workflows and enable natural-language querying of structured healthcare datasets.
Use Cortex Search for semantic and hybrid search across unstructured clinical and operational data.
Implement RAG pipelines using Snowflake-native vector embeddings and the VECTOR data type to ground domain-specific LLMs.
Use Arctic Embed and other Snowflake-native embedding models to build scalable, cost-effective AI features within Snowflake.
Build interactive, production-grade data applications and internal tools using Streamlit in Snowflake.
Develop self-service analytics dashboards and AI-assisted exploration tools embedded in Snowflake.
Create Streamlit front ends for Cortex-powered features so clinical users and analysts can interact with AI outputs in Snowflake.
Develop data transformations, feature-engineering pipelines, and ML preprocessing workflows using Snowpark for Python.
Build and manage scalable ELT pipelines with Snowflake Streams, Tasks, Dynamic Tables, and Snowpipe for real-time and batch ingestion.
Optimize Snowflake performance using micro-partition pruning, clustering keys, materialized views, search optimization, and warehouse right-sizing.
Design and implement Data Vault or Medallion architecture patterns for healthcare data platforms.
Integrate Snowflake with upstream healthcare data sources and downstream BI platforms.
Implement Snowflake-native governance, including row-level security, column masking policies, object tagging, and data classification.
Ensure HIPAA compliance and follow PHI data-handling best practices across Snowflake data assets.
Manage data lineage, cataloging, and access control in alignment with organizational governance frameworks.
Partner with data scientists, ML engineers, clinical analysts, and business stakeholders to translate requirements into Snowflake-native solutions.
Serve as a Snowflake subject matter expert and internal champion by promoting best practices, onboarding teams, and sharing platform capabilities.
Participate in platform roadmap planning and contribute to Snowflake feature adoption strategy.