Description: Senior Engineer - Semantic & Agentic Intelligence Hybrid role (3 days/week in office) at our Salt Lake City, Boise, Fargo, Portland, Vancouver, Renton, Medford, Burlington, Lewiston, Little Rock, and Springdale offices. Preference will be given to current employees and candidates who reside within a commutable distance from our Salt Lake City, Boise, Fargo, Lewiston, Little Rock, and Springdale offices, or who are willing to relocate. Build a career with purpose. Join our Cause to create a person-focused and economically sustainable health care system. Who We Are Looking For: Every day, Cambia's Data & Analytics Engineering Team is living our mission to make health care easier and lives better. Join our Data & Analytics Engineering team as a Senior Engineer on the Semantic & Agentic Intelligence Squad. You will design, build, and own the semantic foundation and agentic AI capabilities that power governed analytics, intelligent automation, and purpose-built data products across the enterprise. You will serve as a technical lead, combining deep dimensional modeling expertise with modern AI/LLM engineering, mentoring engineers, driving technical standards, and contributing to platform strategy. This role positions you for growth into Principal-level engineering, architecture, or people leadership. If you're a motivated Senior Engineer with a passion for making data trustworthy, intelligent, and actionable in healthcare, apply today! What You Bring to Cambia: Qualifications: 6+ years of relevant experience in Data Engineering, Semantic Modeling, or AI/ML Engineering, with senior or lead experience College degree in Computer Science, Mathematics, Business Administration, or a related field A degree may be substituted by sufficient years of experience in the healthcare industry Experience with a supported business area is preferred (e.g., Provider Network, Claims, Membership, Digital Solutions, Pricing) Equivalent combination of education and experience Skills and Attributes: Preferably seeking a technically versatile engineer with deep expertise in dimensional modeling, semantic layer design, and agentic AI development. You bring a consulting-oriented mindset, strong SQL fundamentals, and hands-on experience building governed, observable AI solutions in complex enterprise environments. Preferred Skills: Dimensional Modeling & Data Platform Deep expertise in dimensional modeling with strong command of grain, bridge tables, many-to-many relationships, and slowly changing dimensions (SCDs) Awareness of common data modeling pitfalls and how to prevent them Strong SQL skills on Snowflake or a comparable cloud data warehouse Experience with dbt, ELT/ETL design patterns, and orchestration tools (Airflow or similar) Understanding of data governance, security, compliance, and enterprise platform ownership Semantic Layer & Governed Metrics Experience defining governed metrics in a semantic or metrics layer Has built and deployed a semantic layer (SSAS, LookML, dbt Semantic Layer, or similar) used directly by business users Experience evaluating AI-generated answers and diagnosing and fixing root causes in the semantic layer Agentic AI & LLM Engineering Experience deploying and monitoring AI agents in a governed, regulated environment Built LLM/agentic applications including RAG pipelines, embeddings/vector search, prompt engineering, tool/function calling, and agent orchestration frameworks Experienced with Snowflake Cortex (Analyst, Search, Agents) or comparable native AI tooling Experience developing and managing AI agents for use cases such as: Prior authorization Member support Care gap identification Utilization analytics Anomaly/outlier detection AI Ops & Observability Implemented evaluation frameworks, guardrails, and observability for AI agents overing accuracy, hallucination detection, latency, and cost Familiarity with scoring techniques such as F1 scoring and related evaluation metrics Experience with model/agent monitoring, cost optimization, and performance tuning Healthcare Domain Knowledge Working knowledge of payer data: claims (837/835), eligibility, enrollment, and provider data Familiarity with healthcare standards: HL7/FHIR, ICD-10, CPT/HCPCS Nice to Have: Experience with ontologies, knowledge graphs, or formal business vocabularies Built and managed MLOps pipelines with familiarity with ML models including regression and ensemble methods (e.g., KNN, XGBoost, Random Forest, Decision Trees) Experience building consumer-facing AI applications or features involving inference Experience building multi-tier AI applications or multi-agent workflows, including multimodal LLM experience Experience with statistical and predictive modeling (regression, classification, clustering, time series forecasting) Experience with BI and visualization tools (Tableau, Power BI, Looker) and translating analytical findings into business narratives Exposure to experimentation and hypothesis testing (A/B testing, statistical significance)