Snowflake Data Engineer

EXL; Pittsburgh (Allegheny County); On-site; USD 140,000 - 190,000; Full time

Job summary

EXL seeks a Snowflake Data Engineer to architect and deploy AI-powered data and analytics solutions within the Snowflake platform for healthcare modernization. You will design Cortex-based functions, agents, and semantic search, while building Streamlit-based data apps and dashboards for business stakeholders.

The role emphasizes data modeling, Snowflake Cortex AI capabilities, and seamless integration with cloud services. Collaboration with data scientists and analysts is essential.

Qualifications

• 5+ years of data engineering experience.
• At least 3+ years hands-on Snowflake expertise.
• Experience with Snowflake Cortex AI features: LLM functions, Cortex Analyst, Cortex Search, and AI agent orchestration.
• Hands-on experience building Streamlit in Snowflake applications deployed in production.
• Proficiency in Snowpark for Python including UDFs/UDTFs, stored procedures, and DataFrame API usage.
• Strong SQL skills with deep understanding of Snowflake's execution engine, profiling, and optimization.
• Experience with Snowflake data sharing, Marketplace, and cross-cloud/cross-region replication.
• Familiarity with vector search, embeddings, and RAG in Snowflake contexts.
• Good to have cloud experience: Azure and GCP; connecting Snowflake to cloud-native services.
• Strong understanding of data modeling for analytics (star schema, Data Vault, Medallion/Lakehouse).
• Knowledge of Medicare Stars Patient Safety is a plus.

Responsibilities

• Design and deploy AI-powered Snowflake Cortex solutions for healthcare data use cases.
• Build and orchestrate multi-step AI agents to automate data workflows and enable natural language querying of structured datasets.
• Leverage Cortex Search for semantic and hybrid search across clinical and operational data.
• Implement RAG pipelines using vector embeddings for LLM grounding.
• Use Arctic Embed and Snowflake-native embeddings to build AI features inside Snowflake perimeter.
• Create Streamlit-based front-ends for Cortex-powered features for clinicians and analysts.
• Develop interactive, production-grade data applications and dashboards embedded in Snowflake.
• Build complex data transformations and ML preprocessing pipelines with Snowpark for Python.
• Manage ELT pipelines with Snowflake Streams, Tasks, Dynamic Tables, and Snowpipe for real-time and batch ingestion.
• Optimize performance with micro-partition pruning, clustering keys, materialized views, and warehouse sizing.
• Design Data Vault or Medallion architectures for healthcare platforms.
• Integrate Snowflake with upstream sources and downstream BI platforms.

Skills

5+ years data engineering

3+ years Snowflake

Snowflake Cortex AI

Streamlit in Snowflake apps

Snowpark for Python

SQL proficiency

Data modeling (Data Vault/Medallion)

Vector search/embeddings

Cloud: Azure & GCP

Tools

Snowflake

Streamlit

Snowpark for Python

Azure

GCP

SQL

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.

Responsibilities:

Snowflake AI & Cortex (Core Focus)

• Design and deploy AI-powered solutions using Snowflake Cortex, including LLM-based functions (COMPLETE, SUMMARIZE, SENTIMENT, TRANSLATE, CLASSIFY) for healthcare data use cases.
• 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.
• Leverage Cortex Search for semantic and hybrid search capabilities across unstructured clinical and operational data.
• Implement RAG (Retrieval-Augmented Generation) pipelines using Snowflake's native vector embeddings and VECTOR data type for domain-specific LLM grounding.
• Use Arctic Embed and other Snowflake-native embedding models to build scalable, cost-effective AI features within the Snowflake perimeter.
• Build interactive, production-grade data applications and internal tools using Streamlit in Snowflake (SiS) for business stakeholders and data consumers.
• Develop self-service analytics dashboards and AI-assisted exploration tools embedded directly in the Snowflake environment.
• Create Streamlit-based front-ends for Cortex-powered features — enabling clinical users and analysts to interact with AI outputs without leaving the Snowflake ecosystem.
• Develop complex data transformations, feature engineering pipelines, and ML preprocessing workflows using Snowpark for Python.
• Build and manage scalable ELT pipelines leveraging Snowflake Streams, Tasks, Dynamic Tables, and Snowpipe for real-time and batch ingestion.
• Optimize Snowflake performance through 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.

Data Governance & Security

• Implement Snowflake-native governance including row-level security, column masking policies, object tagging, and data classification.
• Ensure HIPAA compliance and PHI data handling best practices across all 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 robust Snowflake-native solutions.
• Act as a Snowflake subject matter expert and internal champion — driving best practices, onboarding teams, and evangelizing new platform capabilities.
• Participate in platform roadmap planning and contribute to Snowflake feature adoption strategy.

Qualifications:

• 5+ years of data engineering experience, with at least 3+ years of hands-on Snowflake expertise.
• Proven experience with Snowflake Cortex AI features: 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 with 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 into Snowflake.
• Good to have experience with cloud platforms: Azure, and GCP — and connecting Snowflake to cloud-native services.
• Strong understanding of data modeling for analytics (star schema, Data Vault, Medallion/Lakehouse patterns).
• Knowledge of Medicare Stars Patient Safety is a strong plus.