Data Engineer - AI, Agents, & Context - Clinical (Sr. Associate)

Role Description

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 are:

• 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 contributor who builds and maintains core AI/context data capabilities. The role executes key parts of the AI context platform — unstructured ingestion, embeddings, retrieval, and semantic layers — working closely with senior engineers and cross-functional partners to ship reliable, production-grade AI data products.

Key Responsibilities

• Build and contribute to the AI context platform.
• Implement end-to-end pipelines: ingestion → parsing/chunking → enrichment → embeddings → vector indexing → retrieval/serving.
• Build and maintain patterns for incremental refresh, backfills, re-embeddings, deduplication, and lineage across unstructured sources.
• Contribute to retrieval quality improvements (query strategies, hybrid search, metadata filtering) in partnership with AI engineers.
• Deliver semantic and governed data products.
• Implement semantic layers (metrics/entities) that power BI and agent reasoning consistently.
• Apply established data contracts and context contracts for AI inputs (schemas, metadata requirements, freshness, citation expectations).
• Ensure datasets and indexes are documented and reusable.
• Support reliability and performance across assigned workstreams: monitoring, alerting, runbooks, and incident response.
• Contribute to cost and latency optimization across warehouse/lakehouse and vector infrastructure.
• Apply security-by-design patterns: RBAC/ABAC, PII redaction, retention controls, and audit logging.
• Follow established guardrails for AI access to enterprise knowledge in coordination with Security/Legal/Compliance.

Travel Expectations

• Ability to travel as needed up to 4 times per year.

Qualifications

• BA or BS required, preferably in Computer Science, Engineering, or a technology-based discipline.
• 3–6 years in data engineering or data platform roles with strong hands-on delivery.
• Strong SQL and Python (or Scala/Java); solid production engineering habits.
• Experience designing and operating cloud data pipelines at scale.
• Experience working with unstructured data processing and search/retrieval concepts.
• Clear communicator who can work effectively across technical and functional teams.

Preferred Qualifications

• Hands-on experience with vector search and embeddings (pgvector/Pinecone/Weaviate/OpenSearch/Elastic) and retrieval patterns (semantic retrieval, hybrid search, reranking).
• Experience supporting LLM applications (RAG, agent tool interfaces, evaluation/observability).
• Familiarity with knowledge graphs/semantic modeling or metrics layers.
• Experience in regulated environments and data governance programs.

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).
• Consistent application of semantic definitions and context contracts across assigned workstreams.
• Delivery quality: production-ready outputs with minimal rework, well-documented and maintainable.

Behavioral Attributes

• Eager to learn the domain: Proactively builds familiarity with healthcare processes, terminology, and KPIs — can engage credibly with SMEs and ask the right clarifying questions.
• Collaborative and stakeholder-aware: Works well with engineers, consultants, and functional partners; communicates progress and flags risks clearly.
• Consultative problem-solver: Approaches requests with a "diagnose before prescribe" mindset — proposes options and works toward durable solutions rather than one-off fixes.
• 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: Can go deep with engineers and explain concepts plainly to non-technical partners; writes solid docs and runbooks.
• Pragmatic builder: Biases toward shipping value in iterations, validating with users, and improving based on feedback.
• Comfortable with ambiguity: Adapts quickly in evolving AI/data product environments and turns unclear goals into actionable tasks.
• Integrity and stewardship: Handles sensitive data responsibly and respects established governance patterns.

Salary and Benefits

The estimated base salary for this job is $110,000 - $150,000 USD. The range represents a good faith estimate of the range that Huron reasonably expects to pay for this job at the time of the job posting. The actual salary paid to an individual will vary based on multiple factors, including but not limited to specific skills or certifications, years of experience, market changes, and required travel. This job is also eligible to participate in Huron’s annual incentive compensation program, which reflects Huron’s pay for performance philosophy. Inclusive of annual incentive compensation opportunity, the total estimated compensation range for this job is $123,200 - $177,000 USD. The job is also eligible to participate in Huron’s benefit plans which include medical, dental and vision coverage and other wellness programs.