Data Architect - Healthcare Insights, Pharmacy

Job description

Huron helps its clients drive growth, enhance performance and sustain leadership in the markets they serve. We help healthcare organizations build innovation capabilities and accelerate key growth initiatives, enabling organizations to own the future, instead of being disrupted by it. Together, we empower clients to create sustainable growth, optimize internal processes and deliver better consumer outcomes. Health systems, hospitals and medical clinics are under immense pressure to improve clinical outcomes and reduce the cost of providing patient care. Investing in new partnerships, clinical services and technology is not enough to create meaningful and substantive change. To succeed long‑term, healthcare organizations must empower leaders, clinicians, employees, affiliates and communities to build cultures that foster innovation to achieve the best outcomes for patients. Joining the Huron team means you’ll help our clients evolve and adapt to the rapidly changing healthcare environment and optimize existing business operations, improve clinical outcomes, create a more consumer‑centric healthcare experience, and drive physician, patient and employee engagement across the enterprise. Join our team as the expert you are now and create your future. This role sits within a strategic investment to embed AI into how we operate, serve customers, and make decisions within our healthcare business. We're building a healthcare‑wide AI data and context platform with a focus on deep domain expertise embedded throughout our architecture.

Our Goals

Turn structured and unstructured information into trusted, reusable "building blocks" (semantic layers, retrieval services, and agent‑ready interfaces) that accelerate product innovation

Deliver transformational speed and leverage — faster time‑to‑insight, higher automation of knowledge work, and a foundation that scales AI safely and reliably as adoption grows

Unlock new capabilities across our business and create the foundation that drives deeper domain innovation and cross‑domain collaboration

This is a hands‑on technical architect who owns the design and delivery of core AI/context data capabilities. The role is responsible for end‑to‑end architecture decisions across the platform — unstructured ingestion, embeddings, retrieval, semantic layers, and governance — while partnering across engineering, product, and AI teams to ship production‑grade AI data products. Leadership is through technical ownership, design authority, and cross‑functional influence.

Key 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.

Required 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

Preferred Qualifications

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

Example Success Measures

Measurable improvement in AI outcomes: higher retrieval precision/recall, better citation coverage, fewer "missing context" failures

Reduced latency/cost per retrieval and improved platform reliability (SLO attainment, lower MTTR)

Broad adoption of semantic definitions, context contracts, and platform standards across teams

Architecture decisions are well‑documented, defensible, and enable downstream engineers to deliver faster

Platform design earns trust from Security, Compliance, and business stakeholders

Behavioral Attributes

Business‑curious and domain‑eager: Proactively learns healthcare processes, terminology, and KPIs — can speak credibly with SMEs and business leaders, not just translate requirements but help shape the right questions and success measures

Stakeholder‑first collaborator: Builds strong relationships with stakeholders, SMEs, and consultants; clarifies goals, constraints, and tradeoffs early; communicates progress and risks clearly; sets realistic expectations around timelines, scope, and quality

Consultative problem‑solver: Approaches requests with a "diagnose before prescribe" mindset — asks smart questions, proposes options, and guides teams toward durable solutions rather than one‑off fixes

Influence without authority: Leads through expertise and trust — drives alignment, facilitates decisions, and unblocks teams across functions without relying on positional authority

High ownership and follow‑through: Treats reliability, documentation, and operational readiness as part of the work; finishes what they start; holds a high bar for production quality

Clear communicator for mixed audiences: Can go deep with engineers and explain concepts plainly to non‑technical partners; writes crisp architecture docs, designs, and

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Worksite address

chicago, IL, 60290, US

Who can apply

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