AI Data Integration Engineer

InterSources Inc is a certified diverse supplier and global software consultancy that provides digital transformation and technology solutions, including artificial intelligence, cloud migration, data analytics, and cybersecurity services. The AI Data Integration Engineer will build sanitized healthcare claims data layers in Snowflake and integrate LLM-based systems to generate explainable insights, savings opportunities, and recommendations. The role also focuses on HIPAA-compliant data governance, AI pipelines, and collaboration with product, clinical, analytics, and engineering teams.

Responsibilities:

• Design, develop, and maintain sanitized, de-identified views in Snowflake for AI consumption
• Implement robust ETL/ELT transformations to prepare claims, pharmacy, member, and utilization data for downstream AI and analytics workflows
• Build reusable data models, UDFs, and stored procedures to compute derived metrics (e.g., utilization flags, cost drivers, adherence metrics, risk markers)
• Ensure data structures follow HIPAA privacy, minimization, and de-identification best practices
• Collaborate with product and AI teams to define minimal-but-sufficient data schemas for AI analysis
• Build the first version of a claims insight engine using LLMs (Azure OpenAI, OpenAI, or similar) grounded on Snowflake data
• Create schemas and payloads to feed structured data into AI prompts or agent pipelines
• Produce structured outputs such as:
• Patterns in claims behavior
• Cost savings opportunities
• Potential issues in plan design
• Member-level or population-level recommendations
• Explanation narratives suitable for clients or internal teams
• Work with the product team to design LLM prompt strategies, guardrails, and output formatting
• Implement data privacy controls, PHI minimization, and secure access layers within Snowflake
• Work with compliance teams to validate that AI inputs follow regulatory and organizational standards
• Maintain auditability and logging for data access and AI interactions
• Partner with product managers, analysts, and AI engineers to understand requirements and translate them into technical deliverables
• Educate internal teams on how the AI insights layer works (Snowflake view, AI input, AI output patterns)
• Participate in roadmap planning to evolve the claims AI platform into a scalable enterprise solution

Requirements:

• 3+ years of hands-on Snowflake experience, including: SQL, semi-structured data (JSON), UDFs, Secure Views & Masking Policies, Data modeling (Star/Snowflake schemas), Performance optimization
• Strong experience building ETL/ELT pipelines in a modern data stack
• At least 1–2 years of applied AI/LLM development, including: Designing prompt pipelines, Calling LLM APIs (Azure OpenAI, OpenAI, Anthropic, etc.), Integrating structured data into LLM workflows
• Previous work with healthcare or claims datasets (medical or pharmacy)
• Strong understanding of HIPAA privacy principles, de-identification strategy, and PHI minimization
• Clear written and verbal communication
• Ability to translate product requirements into technical solutions
• Comfortable working in a fast-paced, evolving environment
• Highly collaborative; strong problem-solving mindset
• Snowflake, SQL, ETL/ELT tools (DBT, Matillion, Airflow, etc.), Python, APIs, LLMs (Azure OpenAI or OpenAI), Git
• Experience with Azure AI Foundry, including: Model catalog & deployments, Agents / 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 building RAG pipelines, vector embeddings, semantic search
• Experience designing AI-driven analytics, copilot-style solutions, or automated insights tools
• Background in ML Ops, data governance, or data privacy engineering
• PBM or payer experience (pharmacy claims, accumulators, benefit design)
• Experience generating insights such as: Cost driver analysis, Adherence metrics, Trend & utilization insights, Plan optimization / savings recommendations
• Previous work supporting clinical, actuarial, or analytics teams