Time since we first saw it
Day 17
Similar jobs usually come down between day 4 and day 77.
At EXL Service, jobs usually come down by about day 21.
From 51 similar postings that came down, and 281 of EXL Service’s. Coming down is not always a hire.
Snowflake Data Engineer
Reads like an AI Engineer role: building products on existing AI models.
Pittsburgh, Pennsylvania, United States
Live, first seen by us 17 days ago- Pay
- Pay not listed
- Work setting
Hybrid — work setting source
Work setup in the employer’s posting: Hybrid
From the employer’s posting- Employment
Full-time — employment source
Employment type in the employer’s posting: Full time
From the employer’s posting
What you’ll work on
Full postingDesign and deploy AI solutions with Snowflake Cortex LLM functions and agents
Build interactive Streamlit in Snowflake apps for business and clinical users
Develop complex transformations and feature pipelines with Snowpark for Python
Build and manage ELT pipelines with Streams, Tasks, Dynamic Tables and Snowpipe
Implement governance controls such as row-level security and column masking
From the employer’s posting
Design and deploy AI-powered solutions using Snowflake Cortex
Build interactive, production-grade data applications and internal tools using Streamlit in Snowflake (SiS)
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
Implement Snowflake-native governance including row-level security, column masking policies, object tagging, and data classification.
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Oct 27, 202617 days ago
- Recorded sightings
- 28
- Last seen by us
- Nov 13, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
EXL Service is hiring
211 open jobs
Open jobs by the week we first saw them, last 12 weeks.
- Takes jobs down at a usual pace
- 5% of open jobs were reposted
See how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Python
- SQL
- Azure
- GCP
- Snowflake
- Streamlit
Where the posting names them
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.
- 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.
- 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).
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. Snowflake AI & Cortex (Core Focus)
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. 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.
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.
- 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.
- 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.
- 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
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.
- 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 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.
- 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.
- 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
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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
Your next step
Complete your application on fa-ewjt-saasfaprod1.fa.ocs.oraclecloud.com. The employer’s form will show what is required.
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Has EXL Service posted this job before?
Yes. We’ve followed one other Snowflake Data Engineer job there since November 2026. It is still up.
About EXL Service
- People
- About 65,000 in 2025, up 9% from the year before.Annual report (SEC Form 10-K)
- Company
- Listed as EXLS.
Demand for AI Engineer roles
New AI Engineer postings at the employers we follow are up 134% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
About the role
We file this job under AI Engineer, by the work the posting describes.
AI engineers build products on top of AI models that already exist, most often large language models: they connect a model to a company's data and tools and ship the assistants, agents and search features people use.
Postings ask for strong Python or TypeScript, experience with model APIs, retrieval and agent frameworks, and ways to test whether the model's answers are good enough.
An ML engineer trains and serves models; an AI engineer usually starts from a model someone else trained and builds the product around it, though titles often mix the two.
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.
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.
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.
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
Pittsburgh, Pennsylvania, United States
- 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.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.