Data Engineer - AI, Agents, & Context - Revenue Cycle (Associate)

Huron is a consulting company that helps healthcare organizations enhance performance and drive growth. They are seeking a Data Engineer to build and maintain core AI/context data capabilities, focusing on developing a healthcare-wide AI data and context platform that transforms information into reusable components and accelerates product innovation.

Responsibilities:

• Build and contribute to the AI context platform
• Implement end-to-end pipelines: ingestion → parsing/chunking → enrichment → embeddings → vector indexing → retrieval/serving
• Build and maintain patterns for incremental refresh, backfills, re-embeddings, deduplication, and lineage across unstructured sources
• Contribute to retrieval quality improvements (query strategies, hybrid search, metadata filtering) in partnership with AI engineers
• Deliver semantic and governed data products
• Implement semantic layers (metrics/entities) that power BI and agent reasoning consistently
• Apply established data contracts and context contracts for AI inputs (schemas, metadata requirements, freshness, citation expectations)
• Ensure datasets and indexes are documented and reusable
• Support reliability and performance across assigned workstreams: monitoring, alerting, runbooks, and incident response
• Contribute to cost and latency optimization across Snowflake and vector infrastructure
• Apply security-by-design patterns: RBAC/ABAC, PII redaction, retention controls, and audit logging
• Follow established guardrails for AI access to enterprise knowledge in coordination with Security/Legal/Compliance

Requirements:

• Bachelor's Degree in computer science, engineering, or related field of study
• 3–6 years in data engineering or data platform roles with strong hands-on delivery
• Strong SQL and Python (or Scala/Java); solid production engineering habits
• Hands-on experience with Snowflake, including pipeline design, data modeling, and operating at scale in a production environment
• Experience designing and operating cloud data pipelines at scale
• Experience working with unstructured data processing and search/retrieval concepts
• Clear communicator who can work effectively across technical and functional teams
• Hands-on experience with vector search and embeddings (pgvector/Pinecone/Weaviate/OpenSearch/Elastic) and retrieval patterns (semantic retrieval, hybrid search, reranking)
• Experience supporting LLM applications (RAG, agent tool interfaces, evaluation/observability)
• Familiarity with knowledge graphs, semantic modeling, or metrics layers
• Experience in regulated environments and data governance programs
• Exposure to dbt, Iceberg, or other lakehouse/semantic layer tooling alongside Snowflake