Data Architect - Market (Manager)

Huron helps its clients drive growth and enhance performance in the healthcare sector. The Data Architect will design and deliver core AI data capabilities, ensuring the architecture supports innovative healthcare solutions and optimizes business operations.

Responsibilities:

• Architect and own the AI context platform Design 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
• Design and deliver semantic and governed data products 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 infrastructure
• Set 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

Requirements:

• 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
• Experience supporting LLM applications (RAG, agent tool interfaces, evaluation/observability)
• Knowledge of knowledge graphs, semantic modeling, or metrics layers at scale
• Experience in regulated environments and mature data governance programs
• Familiarity with Iceberg, Delta Lake, or other open table formats in a lakehouse context
• Prior experience in a formal or informal technical lead or staff engineer capacity