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 covering 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)
