> Markdown version of [/jobs/ext/1255894-data-engineer-ai-agents-context-revenue](https://www.wearedevelopers.com/jobs/ext/1255894-data-engineer-ai-agents-context-revenue). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer - AI, Agents, & Context - Revenue... - **Company:** Huron Consulting Group Inc. - **Location:** Chicago, IL, United States (Remote available) - **Experience:** Experienced - **Salary:** $95,000.0 - $130,000.0 - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Artificial Intelligence, Audit Trail, Data Deduplication, Information Engineering, Data Governance, Data Infrastructure, Graph Database, Python (Programming Language), Role-Based Access Control, Power BI, Standard Sql, Search Technologies, Unstructured Data, Large Language Models, Snowflake, Mttr, Information Technology - **Published:** July 13, 2026 - **Apply:** https://www.juju.com/job/00000000gfz422 ## About the Role + Ability to travel as needed up to 4 times per year., + Bachelor's Degree in computer science, engineering, or related field of study + 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 + Hands-on experience with Snowflake, including pipeline design, data modeling, andoperatingat scale in a production environment + 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 + Exposure todbt, Iceberg, or otherlakehouse/semantic layer tooling alongside Snowflake 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 ## Description _Build and contribute to the AI context platform_ + Implement end-to-end pipelines: ingestion * parsing/chunking * enrichment * embeddings * vector indexing * retrieval/serving + Build andmaintainpatterns 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 _Operational excellence_ + Support reliability and performance across assigned workstreams: monitoring, alerting, runbooks, and incident response + Contribute to cost and latency optimization across Snowflake and vector infrastructure _AI safety and compliance_ + 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, + **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,validatingwith users, and improving based on feedback ## Related Videos - [Beyond Dashboards: Fixing Text-to-SQL with Semantic RAG](https://www.wearedevelopers.com/videos/2036-beyond-dashboards-fixing-text-to-sql-with-semantic-rag) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [How Cisco embraced a DevOps culture within its network engineering team](https://www.wearedevelopers.com/videos/99-how-cisco-embraced-a-devops-culture-within-its-network-engineering-team) - [What Developers Get Wrong About Application Quality](https://www.wearedevelopers.com/videos/233-what-developers-get-wrong-about-application-quality) - [Hacking AI at the Edge of the Indian Ocean](https://www.wearedevelopers.com/videos/100177-hacking-ai-at-the-edge-of-the-indian-ocean) - [Beyond SQL Generation: How to Teach Agents What Your Database Actually Means](https://www.wearedevelopers.com/videos/100127-beyond-sql-generation-how-to-teach-agents-what-your-database-actually-means) ## Related Articles - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know)