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.
ResponsibilitiesSnowflake 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.
Streamlit & Application Development
- 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.
Snowpark & Data Engineering
- 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.
Collaboration & Platform Ownership
- 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.
- 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.
Skills Required
- 5+ years of data engineering experience
- 3+ years of hands-on Snowflake expertise
- Experience with Snowflake Cortex AI features, including LLM functions, Cortex Analyst, Cortex Search, and AI agent orchestration
- Experience building and deploying production Streamlit in Snowflake applications
- Proficiency with Snowpark for Python, including custom UDFs, UDTFs, stored procedures, and the DataFrame API
- Strong SQL skills and knowledge of Snowflake execution, query profiling, and optimization
- Experience with Snowflake data sharing, Marketplace, and cross-cloud or cross-region replication
- Familiarity with vector search, embeddings, and RAG architecture patterns
- Experience with Azure and GCP cloud platforms and cloud-native service integrations
- Understanding of star schema, Data Vault, and Medallion or Lakehouse data modeling patterns
- Knowledge of Medicare Stars Patient Safety
EXL Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about EXL and has not been reviewed or approved by EXL.
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Healthcare Strength — Medical plan options include HSA, HRA, and PPO choices, with additional health-related programs such as disability coverage and an employee assistance program. Feedback suggests these options provide meaningful flexibility and coverage breadth for many roles.
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Leave & Time Off Breadth — Up to 20 days of paid time off, plus federal holidays and paid sick and safe time, are highlighted. Feedback suggests this leave structure is a notable benefit alongside core compensation.
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Retirement Support — A traditional and Roth 401(k) with up to a 3% company match and full vesting after one year is described. Feedback suggests this match and vesting timeline help bolster long-term savings.
EXL Insights
What We Do
Choosing a digital partner is about more than capabilities — it’s about collaboration and character. Unrealistic overhauls and off-the-shelf products ignore what matters most — your unique needs, culture, goals, and your legacy data and technology environments. At EXL, our collaboration is built on ongoing listening and learning to adapt our methodologies. We’re your business evolution partner—tailoring solutions that make the most of data to make better business decisions and drive more intelligence into your increasingly digital operations. Whether your goals are scaling the use of AI and digital, redesign operating models, or driving better and faster decisions, we’re here to partner with you to help you gain—and maintain—competitive advantage with efficient, sustainable models at scale. Our expertise in transformation, data science, and change management helps make your business more efficient and effective, improve customer relationships and enhance revenue growth. Instead of focusing on multi-year, resource- and time-intensive platform designs or migrations, we look deeper at your entire value chain to integrate strategies with impact. We use our specialization in analytics, digital interventions, and operations management—alongside deep industry expertise — to deliver solutions that help you outperform the competition. At EXL, it’s all about outcomes—your outcomes—and delivering success on your terms. Share your goals with us and together, we’ll optimize how you leverage data to drive your business forward. For more information, visit www.exlservice.com.
