AI Data Integration Engineer

We are seeking an experienced AI Data Integration Engineer with strong hands-on expertise in Snowflake, healthcare claims data, and AI/LLM integration. The role will build secure, sanitized healthcare data layers and pipelines that enable AI systems to generate insights, identify patterns, optimize costs, and support business decisions.

Read the overview of this opportunity to understand what skills, including and relevant soft skills and software package proficiencies, are required.

The ideal candidate will combine data engineering, AI/LLM development, and healthcare domain expertise, with experience integrating AI solutions using Azure OpenAI and/or Azure AI Foundry.

Design and develop secure Snowflake views, data models, and ETL/ELT pipelines for healthcare claims data.

Build sanitized data layers using SQL, JSON, UDFs, secure views, and masking policies .

Integrate structured healthcare data with LLMs, APIs, and AI workflows .

Develop prompt pipelines and AI-driven analytics using Azure OpenAI, OpenAI, or Anthropic .

Apply HIPAA, PHI minimization, and healthcare data de-identification practices.

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

Develop AI use cases such as cost-driver analysis, utilization trends, adherence insights, and savings opportunities .

Optimize Snowflake performance, data quality, security, and scalability.

Document solutions and build reproducible, maintainable data and AI pipelines.

Strong SQL, Python, JSON, UDFs, secure views, masking policies, and data modeling

~ Experience building ETL/ELT pipelines using tools such as dbt, Matillion, or Airflow

~1–2+ years of applied AI/LLM development

~ Experience with LLM APIs, prompt engineering, and integrating structured data into AI workflows

~ Experience working with healthcare claims data , preferably medical or pharmacy claims

~ Strong understanding of HIPAA, PHI, data privacy, and de-identification

~ Experience with APIs, Git, and modern data engineering practices

Azure AI Foundry – models, deployments, agents, tool calling, and retrieval

RAG, embeddings, vector databases, and semantic search

Azure Functions, Service Bus, Microsoft Fabric, FHIR APIs

Databricks and healthcare data platforms

PBM/payer experience and pharmacy claims

AI copilots, automated insights, or AI-driven analytics

MLOps, data governance, and privacy engineering

Secure and validated Snowflake healthcare data layer successfully deployed.

Reliable AI/LLM integration producing accurate and explainable insights.

Reproducible, well-documented data and AI pipelines.

Successful AI prototype/pilot using Azure AI Foundry. xhyhwjd

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