Weekend Data Engineer Contract Jobs in Elgin, IL

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 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

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

Required Qualifications

• 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

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,validatingwith 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 respectsestablishedgovernance patterns