Data Architect - Market (Manager)

Huron is a consulting company focused on helping healthcare organizations enhance performance and drive growth. The Data Architect - Market (Manager) role involves owning the design and delivery of core AI data capabilities, partnering across teams to optimize healthcare operations and improve clinical outcomes.

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
• Operational excellence
• 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)
• AI safety, governance, and compliance
• 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
• Lead through influence
• 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
• Future Scope
• This role is expected to grow into direct people leadership over time. As the platform matures and the engineering team expands, the Architect will take on formal responsibility for leading a small team of engineers — owning hiring input, technical development, and delivery oversight. Candidates should be comfortable with that trajectory and motivated by the opportunity to build and shape a team from an early stage
• Travel Expectations
• Ability to travel as needed up to 4 times per year

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