AI Data Integration Engineer, RCM Systems

AI Data Integration Engineer, RCM Systems

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.

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