Design and build integrations connecting legacy practice management systems to an AI platform. Responsibilities include AI-assisted schema mapping, data modeling, ETL pipelines, REST/SOAP APIs, data cleansing, quality monitoring, production troubleshooting, documentation, and collaboration across architecture, product, engineering, and operations teams. The role requires applying LLM tools to schema inference, field reconciliation, anomaly detection, and data-quality automation while handling healthcare data securely.
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.
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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