Description

Job Summary

STAFFXPERT LLC is seeking an AI Data Integration Engineer on behalf of our client in New Orleans, LA to support the development of secure, sanitized healthcare claims data solutions and practical AI/LLM integrations.

This role sits at the intersection of data engineering, AI development, and healthcare domain expertise. The engineer will help build Snowflake-based data layers, ETL/ELT pipelines, and AI/LLM workflows that enable internal and external systems to identify insights, patterns, savings opportunities, and potential issues for members and clients.

The ideal candidate brings hands-on experience with Snowflake, healthcare claims data, AI/LLM development, and HIPAA privacy principles, with Azure AI Foundry experience being a strong plus.

Key Responsibilities

Build and maintain sanitized healthcare claims data layers using Snowflake views, models, and pipelines.

Develop and optimize ETL/ELT pipelines within a modern data stack.

Work with structured and semi-structured data, including JSON.

Develop AI/LLM workflows that integrate structured data with LLM capabilities.

Design prompt pipelines and integrate LLM APIs such as Azure OpenAI, OpenAI, or Anthropic.

Support AI-driven analytics designed to identify insights, patterns, savings opportunities, and potential issues.

Apply secure data practices, including de-identification, PHI minimization, masking policies, and secure views.

Collaborate with product, clinical, analytics, and engineering teams to translate requirements into technical solutions.

Support prototypes or pilots for future enterprise AI capabilities, including Azure AI Foundry.

Required Qualifications

3+ years of hands-on Snowflake experience, including:

SQL

Semi-structured data (JSON)

UDFs

Secure Views and Masking Policies

Star/Snowflake data modeling

Performance optimization

Strong experience building ETL/ELT pipelines using modern data-stack technologies.

1–2 years of applied AI/LLM development experience.

Experience designing prompt pipelines and working with LLM APIs.

Experience integrating structured data into LLM workflows.

Previous experience working with healthcare or claims datasets, including medical or pharmacy claims.

Strong understanding of HIPAA privacy principles, de-identification, and PHI minimization.

Strong Python and API development experience.

Experience with Git.

Clear written and verbal communication skills.

Ability to translate product requirements into technical solutions.

Strong collaboration and problem-solving skills.

Preferred Qualifications

Experience with Azure AI Foundry, including:

Model catalogs and deployments

Agents and tool-calling workflows

Responses API

Vector indexing and retrieval

Experience with Azure OpenAI in a HIPAA environment.

Familiarity with Health Data Services, FHIR, or Fabric Healthcare Data Solutions.

Experience with RAG pipelines, vector embeddings, and semantic search.

Experience developing AI-driven analytics, copilot-style solutions, or automated insights tools.

Background in ML Ops, data governance, or data privacy engineering.

PBM or payer experience, including pharmacy claims, accumulators, or benefit design.

Experience supporting clinical, actuarial, or analytics teams.

Experience with Azure Functions, Azure Service Bus, Fabric, FHIR APIs, Databricks, or vector databases.

Technical Environment

Required: Snowflake, SQL, Python, ETL/ELT tools such as DBT, Matillion, or Airflow, APIs, LLMs, Azure OpenAI/OpenAI, and Git.

Preferred: Azure AI Foundry, Azure Functions, Azure Service Bus, Fabric, FHIR APIs, Databricks, and Vector DBs.

What Success Looks Like

Secure and validated Snowflake data views and pipelines.

Consistent, accurate, and explainable AI-generated insights.

Reproducible data and AI workflows with strong documentation.

Successful AI prototypes or pilots supporting future enterprise capabilities.

Effective collaboration with product, clinical, analytics, and engineering teams.

Key Skills

Education

Any Graduate