Healthcare Data Engineer, Claims and OMOP

Job description

A leading healthcare organization is seeking a Data Engineer to build and modernize large-scale healthcare data pipelines supporting claims, member, and person data. The position will focus on incremental data processing, ETL optimization, identity resolution, data quality, and scalable distributed processing.

The Data Engineer will work with engineering, data architecture, and product stakeholders to convert existing processing patterns into reliable, production-ready solutions while supporting broader data platform modernization.

Responsibilities

• Design and implement incremental processing workflows for member and person datasets.
• Develop, maintain, and optimize large-scale ETL pipelines for healthcare and claims data.
• Create processing rules for records containing missing, null, or changing identity identifiers.
• Analyze existing identity resolution and person-matching processes and identify opportunities for improvement.
• Build reconciliation and data-validation processes that compare incremental processing results with historical full-refresh outputs.
• Partner with data modeling and architecture teams to implement and operationalize data model changes.
• Contribute to data platform modernization and Databricks migration initiatives.
• Optimize pipeline performance and scalability across large datasets.
• Troubleshoot data issues and implement data-quality remediation.
• Develop technical documentation, solution designs, and operational runbooks.
• Participate in Agile delivery activities, backlog refinement, code reviews, and production support.

Required experience and skills

• Advanced SQL development and data transformation experience.
• Experience designing and developing large-scale ETL pipelines in cloud environments.
• Experience working with healthcare data, particularly claims information.
• Strong data analysis, troubleshooting, reconciliation, and data-quality remediation skills.
• Experience implementing incremental data-processing patterns.
• Experience addressing identity resolution and person-matching scenarios within data pipelines.
• Experience with large-scale data processing technologies such as Databricks, Spark, or equivalent distributed platforms.
• Experience working with data models and implementing changes across production data pipelines.
• Experience working within Agile delivery teams using Jira and related development tools.
• Strong written and verbal communication skills, including the ability to clearly document technical designs and operational processes.