Senior Data Engineer

Senior Data Engineer – Snowflake & Agentic AI

Must live in Oregon, Washington, Utah, Idaho, North Dakota, and Minnesota

We’re looking for a Senior Data Engineer with strong experience in Snowflake, SQL, Python, semantic modeling, and Agentic AI to build scalable data and AI solutions within a healthcare environment. This role combines hands-on engineering with technical leadership, focusing on governed data platforms, LLM applications, and healthcare payer data.

• What You’ll Be Doing
• Lead and mentor Data and AI Engineers while establishing engineering best practices.
• Design and build dimensional models, semantic layers, and governed business metrics.
• Develop and deploy AI agents, RAG pipelines, vector search, and LLM applications.
• Implement AI evaluation, guardrails, monitoring, and observability.
• Optimize Snowflake performance, SQL queries, and platform costs.
• Build and maintain ETL/ELT pipelines using Python, SQL, dbt, and orchestration tools.
• Collaborate with cross-functional teams to deliver reliable, compliant data and AI solutions.
• Required Qualifications
• 6+ years in Data Engineering, Semantic Modeling, or AI/ML Engineering, including senior or lead experience.
• Expert-level SQL and strong Python skills, with Snowflake or comparable cloud data warehouse experience.
• Experience with dbt, ETL/ELT pipelines, and orchestration tools such as Airflow.
• Hands-on experience deploying and monitoring AI agents in regulated environments.
• Experience with RAG, embeddings, vector search, prompt engineering, function calling, and agent orchestration.
• Strong understanding of dimensional modeling and semantic layer design.
• Healthcare payer experience, including claims (837/835), eligibility, enrollment, provider data, and familiarity with HL7/FHIR, ICD-10, and CPT/HCPCS.
• Preferred Qualifications
• Experience with Snowflake Cortex Analyst, Search, or Agents.
• Experience building semantic layers using LookML, SSAS, or dbt Semantic Layer.
• Knowledge of SCDs, bridge tables, grain, and many-to-many relationships.
• Experience with healthcare areas such as Claims, Membership, Provider Network, or Pricing.
• Understanding of data governance, security, and compliance.
• Nice to Have
• Experience with MLOps, knowledge graphs, multi-agent workflows, or multimodal LLMs.
• AI Ops experience with evaluation frameworks, hallucination detection, observability, and cost optimization.
• Healthcare AI use cases such as prior authorization, member support, care gaps, or anomaly detection.
• Experience with predictive modeling, Power BI, Tableau, Looker, or A/B testing.