Data Architect – Market

Huron

Architect for AI context platform driving data innovation and collaboration in healthcare at Huron. Involved in end-to-end architecture design, leading cross-functional initiatives and technical roadmap.

Posted 5/13/2026full-timeRemote • Illinois • 🇺🇸 United StatesSeniorLead💰 $140,000 - $190,000 per yearWebsite

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformJavaPythonScalaSQL

About the role

Key responsibilities & impact

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
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

What you’ll need

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.

Benefits

Comp & perks

medical, dental and vision coverage
annual incentive compensation program
401(k) plan with generous employer match
employee stock purchase plan
generous Paid Time Off policy
paid parental leave
adoption assistance
wellness programs

ATS Keywords

✓ Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

SQLPythonScalaJavaSnowflakevector searchembeddingsdbtCI/CDdata modeling

Soft Skills

leadershipcommunicationcross-functional collaborationmentoringalignment building