AI Data Integration Engineer, RCM Systems
# AI Data Integration Engineer, RCM Systems **Harris** · United States · `Remote` · `Full Time` 💼 **Уровень роли:** `Senior` 🕒 **Статус:** *Опубликовано: сегодня* · *Источник: Himalayas (JSON API)* --- ### Top Skills & Match 🎯 **Ключевой стек роли:** `[AI-Integration-Engineer]` `[Data-Integration-Engineer]` `[ETL-Engineer]` `[Integration-Engineer]` `[Data-Engineer]` `[AI-ML-Integration-Engineer]` `[Senior-Data-Integration-Engineer]` `[AI-Data-Engineer]` --- ### About the Role You'll use it to turn raw legacy schemas and documentation into first-pass mapping specs, reconcile inconsistent field names and codes across systems, and catch data-quality problems that manual review would miss before they hit production. Key Responsibilities - Lead the architectural design of integration strategies and solutions that connect various internal and external systems to our central platform. - AI-accelerated mapping & data quality: use AI/LLM tools to speed up schema mapping, field reconciliation, and anomaly detection. - Data modeling & mapping: build data models and source-to-target mapping specs from Practice Management (PM) systems to our AI platform. - Pipelines: design and build ETL processes and REST/SOAP APIs that move data into Resolv Core, per the architect's design. - Data wrangling: cleanse, structure, and enrich source data into Resolv Core's target format. - Pave the path where there's no existing playbook; several of these legacy systems are poorly documented. - Reliability: monitor, troubleshoot, and resolve integration issues in production. - Collaboration: work closely with our AI Architect, product, engineering, operations, and leadership. - Documentation: maintain data models, mapping specs, and pipeline configurations. Qualifications Experience: A minimum of 5 years in data/system integration or ETL engineering, building production integrations against complex legacy systems. Healthcare/RCM experience preferred; direct exposure to one or more Practice Managenent (PM) systems. Technical (AI first): - Hands-on experience using AI/LLM APIs (e.g., OpenAI, Azure OpenAI) for schema inference, field-mapping/entity resolution, or automated data-quality checks — with concrete examples. - Judgment on the best approach to using AI tooling. - Production-grade proficiency with SQL and experience with relational databases. - Familiarity with Python and JavaScript (or similar scripting language). - Design, build, and maintain ETL processes, data pipelines, and APIs to facilitate the seamless flow of data between different applications and data sources. - REST and SOAP API development. - Comfortable with JSON, XML, CSV, flat-file, and EDI formats. - Data modeling and mapping-spec authorship. - Understanding of HIPAA and PHI security practices. - Cloud integration platforms; Azure stack (Fabric, Data Lake, SQL, Data Factory) a plus. - HL7/FHIR knowledge. Originally posted on Himalayas
- AI-Integration-Engineer
- Data-Integration-Engineer
- ETL-Engineer
- Integration-Engineer
- Data-Engineer
- AI-ML-Integration-Engineer
- Senior-Data-Integration-Engineer
- AI-Data-Engineer
Наблюдалась 2026-10-06, впервые 2026-10-06, источник — Himalayas (JSON API).