Data Engineer- AI, Agents, & Context- Revenue Cycle (Associate)

Requisitos e responsabilidades

Conteúdo da vaga extraído em seções para revisão mais rápida.

Details

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
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 Snowflake 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
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
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
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
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