Healthcare AI Data Architect & Platform Lead

Huron; Chicago (IL); Sur place; USD 140 000 - 190 000; Plein temps

Résumé du poste

Huron is seeking an experienced AI data architecture leader to own the design and delivery of a healthcare AI context platform. You will drive end-to-end architecture—from ingestion and embeddings to retrieval, semantic layers, and governance—while partnering with engineering, product, and AI teams to ship production-grade data products.

You will define scalable data patterns, ensure security-by-design, and guide cross-functional initiatives across Snowflake, lakehouse, and vector technologies.

Qualifications

• 8–12+ years in data engineering, data architecture, or platform roles with significant hands-on delivery.
• Expert SQL and strong Python (or Scala/Java); deep production engineering habits
• Hands-on Snowflake expertise including advanced data modeling, pipeline design, performance tuning, and operating at scale in production
• Proven experience designing cloud data architectures on AWS, Azure, or GCP — including storage, compute, orchestration, and networking considerations
• Hands-on experience with vector search and embeddings (pgvector/Pinecone/Weaviate/OpenSearch/Elastic) and retrieval patterns (semantic retrieval, hybrid search, reranking)
• Experience with dbt or comparable semantic layer tooling in a production environment
• Demonstrated ability to lead cross-functional technical initiatives and drive alignment across teams
• Strong written and verbal communication skills — able to present architecture decisions to both technical and non-technical audiences

Responsabilités

• Architect end-to-end platform architecture: ingestion → parsing/chunking → enrichment → embeddings → vector indexing → retrieval/serving
• Define scalable patterns for incremental refresh, backfills, re-embeddings, deduplication, and lineage across unstructured sources
• Set technical direction for retrieval quality (query strategies, hybrid search, metadata filtering, reranking) in partnership with AI engineers
• Evaluate and select infrastructure, tooling, and cloud services to support platform needs across AWS/Azure/GCP environments
• Architect and implement semantic layers (metrics/entities) that power BI and agent reasoning consistently across the platform
• Define data contracts and context contracts for AI inputs (schemas, metadata requirements, freshness, citation expectations)
• Establish standards for discoverability, documentation, and reusability across datasets and indexes
• Own the dbt or semantic layer tooling strategy and ensure consistent application across workstreams
• Own reliability and performance at the platform level: monitoring, alerting, SLAs/SLOs, runbooks, incident response, and postmortems
• Drive cost and latency optimization across Snowflake, lakehouse, and vector infrastructureSet engineering standards for CI/CD, testing, and evaluation (retrieval eval sets, regression tests, online telemetry)
• Implement security‑by‑design: RBAC/ABAC patterns, PII redaction, retention controls, audit logging, and safe access pathways for agent tools
• Partner with Security/Legal/Compliance to define and enforce guardrails for AI access to enterprise knowledge
• Own governance patterns for sensitive data handling across the platform
• Drive technical roadmap decomposition with product, AI, and application stakeholders
• Facilitate architectural decisions across teams and functions, building alignment without direct authority
• Set best practices and mentor engineers via design reviews, code reviews, and documentation
• This role is expected to grow into direct people leadership over time.

Connaissances

SQL

Python

Outils

Snowflake

dbt

AWS

Azure

GCP

Vector search

Description du poste

Huron is seeking an experienced AI data architecture leader to own the design and delivery of a healthcare AI context platform. You will drive end-to-end architecture—from ingestion and embeddings to retrieval, semantic layers, and governance—while partnering with engineering, product, and AI teams to ship production-grade data products.

You will define scalable data patterns, ensure security-by-design, and guide cross-functional initiatives across Snowflake, lakehouse, and vector technologies.