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# Data Engineer

- **Company:** Knowtion Health
- **Location:** United States (Remote available)
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** .NET Framework, Artificial Intelligence, Amazon Web Services, Cloud Database, Continuous Integration, Data Validation, Information Engineering, Data Governance, Extract Transform Load (ETL), Github, Python (Programming Language), Standard Sql, SQL Databases, Data Storage Technologies, Snowflake, Data Lineage, Data Management, Software Version Control, Data Pipelines, Legacy Systems
- **Published:** May 14, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=2a9f60a8ee9132fd

## About the Role

* 2-5 years of experience in data engineering or a related field
 * Hands-on experience with dbt, Snowflake, and cloud platforms (preferably AWS)
 * Strong SQL
 * Moderate Python skills
 * Experience with healthcare data, especially revenue cycle management, is highly preferred
 * Familiarity with ETL tools, CI/CD workflows, and version control systems (e.g., GitHub)
 * Exposure to data governance and compliance frameworks (e.g., HIPAA)

The above statements are intended to provide the general nature and level of work being performed by most people assigned to the position. They are not intended to be an exhaustive list of all responsibilities, duties and requirements.

## Description

* Data Pipeline Development: Design, build, and maintain robust ETL/ELT pipelines using dbt, Python, and SQL to ingest and transform healthcare data from diverse sources.
 * Cloud Data Engineering: Work within AWS and Snowflake environments to manage data storage, processing, and access for analytics and operational use cases.
 * Collaboration: Partner with data scientists, analysts, and application developers to deliver clean, well-modeled data that supports AI/ML, reporting, and business intelligence.
 * Healthcare Data Integration: Develop solutions that integrate healthcare revenue cycle data (e.g., 835/837 files, claim and denial data) into centralized data platforms.
 * Data Quality and Governance: Implement data validation, lineage tracking, and governance practices to ensure accuracy, compliance, and traceability.
 * Operational Enablement: Support downstream systems such as Artiva and .NET applications by delivering curated datasets and flat files from Snowflake.
 * Continuous Improvement: Contribute to the modernization of legacy systems and participate in initiatives to unify data platforms across the organization.


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