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

Huron helps healthcare organizations drive growth, improve performance, optimize operations, and deliver better outcomes through innovation and technology. The Associate Data Engineer will build and maintain core AI context platform capabilities, including unstructured data ingestion, embeddings, retrieval, semantic layers, governance, reliability, and production-grade data products.

Responsibilities:

• 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
• 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
• Ability to travel as needed up to 4 times per year

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