{"schemaVersion":"jobsearcher.job.v1","id":"21132db84f9baf44cf17f06e","url":"https://jobsearcher.com/jobs/21132db84f9baf44cf17f06e","canonicalUrl":"https://jobsearcher.com/jobs/21132db84f9baf44cf17f06e","title":"Data Engineer III","description":"Location: Remote – United StatesEmployment Type: Long-Term Contract / Potential Contract-to-HireClient: LaunchCode Client Partner – Enterprise Healthcare OrganizationPosition OverviewLaunchCode is seeking an experienced Data Engineer III for a client partner in the healthcare industry. This role will focus on building and managing enterprise Data Supply Chain pipelines that support operational, analytical, real-time data exchange, and AI-readiness initiatives.The ideal candidate is a hands-on Data Engineer with strong experience designing ETL/data pipelines, integrating complex data sources, and working within modern cloud data environments including Databricks and Snowflake.What You’ll DoDesign, build, maintain, and optimize enterprise Data Supply Chain pipelines supporting operational and analytical needs.Manage both real-time data flows and high-volume/bulk data exchange.Develop data integration and transformation solutions using Databricks, Snowflake/Snowpipe, and modern ETL technologies.Apply strong historical ETL experience using tools such as Informatica or Talend.Transform and model structured and semi-structured data, including JSON, XML, flat files, FHIR, HL7, and relational database data.Build pipelines that move data into Databricks and relational structures within Snowflake.Support application-to-application and database-to-database integrations.Design and support APIs that enable enterprise data access and integration.Work with cloud and data technologies including AWS S3, MongoDB, GraphDB, Databricks, and Snowflake.Establish and follow standards for data modeling, data quality, security, metadata, completeness, and end-to-end integration.Support development of a Longitudinal Health Record, enabling enterprise access to healthcare data for operational, analytical, and AI use cases.Help ensure enterprise data is accessible, trustworthy, standardized, and prepared for future AI initiatives.Potentially support API accessibility and data services through MongoDB.Technical EnvironmentStrong experience with several of the following is expected:DatabricksSnowflake / SnowpipeETL and enterprise data pipeline engineeringInformatica and/or TalendSQL and relational data structuresAWS S3MongoDBGraph databases / GraphDBJSONXMLFlat-file processingAPI development and integrationApplication-to-database and database-to-database integrationFHIRHL7 V3 / V4Data modelingData quality and governanceMetadata and security standardsHealthcare data experience, particularly working with FHIR, HL7, longitudinal healthcare records, or healthcare interoperability, is highly valuable.AI / Modern Engineering EnvironmentThe organization is also incorporating AI-assisted engineering capabilities. Exposure to or awareness of tools such as Devin, Windsurf, and AI capabilities within Databricks is beneficial.This does not need to be an AI Engineer, but the ideal candidate should be comfortable working in an engineering organization increasingly using AI-assisted development tools.What We’re Really Looking ForThis position requires more than familiarity with a list of technologies.Candidates should be able to clearly explain:A data pipeline they personally designed or builtWhere the data originated and where it ultimately landedHow data was ingested, transformed, modeled, and validatedWhether the pipeline handled batch, bulk, or real-time dataThe ETL tools and architecture they selected and whyHow they handled data quality and failuresHow APIs or databases were integratedTheir specific hands-on responsibilitiesWhat went wrong and how they troubleshot itHow they worked with Databricks, Snowflake, Informatica, Talend, or comparable technologies in a production environmentWe are looking for someone who can walk through the engineering from source to destination, not simply identify the technologies on their résumé.","company":"Launchcode","rawCompany":"launchcode","city":"St Louis","state":"MO","isRemote":false,"isActive":false,"createdAt":"2026-09-04T08:24:22.941Z","occupations":[{"code":"15-1243.01","title":"Data Warehousing Specialists","slug":"data-warehousing-specialists"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1243.00","title":"Database Architects","slug":"database-architects"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"518210","title":"Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services","slug":"computing-infrastructure-providers-data-processing-web-hosting-and-related-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Data Engineer III","description":"Location: Remote – United StatesEmployment Type: Long-Term Contract / Potential Contract-to-HireClient: LaunchCode Client Partner – Enterprise Healthcare OrganizationPosition OverviewLaunchCode is seeking an experienced Data Engineer III for a client partner in the healthcare industry. This role will focus on building and managing enterprise Data Supply Chain pipelines that support operational, analytical, real-time data exchange, and AI-readiness initiatives.The ideal candidate is a hands-on Data Engineer with strong experience designing ETL/data pipelines, integrating complex data sources, and working within modern cloud data environments including Databricks and Snowflake.What You’ll DoDesign, build, maintain, and optimize enterprise Data Supply Chain pipelines supporting operational and analytical needs.Manage both real-time data flows and high-volume/bulk data exchange.Develop data integration and transformation solutions using Databricks, Snowflake/Snowpipe, and modern ETL technologies.Apply strong historical ETL experience using tools such as Informatica or Talend.Transform and model structured and semi-structured data, including JSON, XML, flat files, FHIR, HL7, and relational database data.Build pipelines that move data into Databricks and relational structures within Snowflake.Support application-to-application and database-to-database integrations.Design and support APIs that enable enterprise data access and integration.Work with cloud and data technologies including AWS S3, MongoDB, GraphDB, Databricks, and Snowflake.Establish and follow standards for data modeling, data quality, security, metadata, completeness, and end-to-end integration.Support development of a Longitudinal Health Record, enabling enterprise access to healthcare data for operational, analytical, and AI use cases.Help ensure enterprise data is accessible, trustworthy, standardized, and prepared for future AI initiatives.Potentially support API accessibility and data services through MongoDB.Technical EnvironmentStrong experience with several of the following is expected:DatabricksSnowflake / SnowpipeETL and enterprise data pipeline engineeringInformatica and/or TalendSQL and relational data structuresAWS S3MongoDBGraph databases / GraphDBJSONXMLFlat-file processingAPI development and integrationApplication-to-database and database-to-database integrationFHIRHL7 V3 / V4Data modelingData quality and governanceMetadata and security standardsHealthcare data experience, particularly working with FHIR, HL7, longitudinal healthcare records, or healthcare interoperability, is highly valuable.AI / Modern Engineering EnvironmentThe organization is also incorporating AI-assisted engineering capabilities. Exposure to or awareness of tools such as Devin, Windsurf, and AI capabilities within Databricks is beneficial.This does not need to be an AI Engineer, but the ideal candidate should be comfortable working in an engineering organization increasingly using AI-assisted development tools.What We’re Really Looking ForThis position requires more than familiarity with a list of technologies.Candidates should be able to clearly explain:A data pipeline they personally designed or builtWhere the data originated and where it ultimately landedHow data was ingested, transformed, modeled, and validatedWhether the pipeline handled batch, bulk, or real-time dataThe ETL tools and architecture they selected and whyHow they handled data quality and failuresHow APIs or databases were integratedTheir specific hands-on responsibilitiesWhat went wrong and how they troubleshot itHow they worked with Databricks, Snowflake, Informatica, Talend, or comparable technologies in a production environmentWe are looking for someone who can walk through the engineering from source to destination, not simply identify the technologies on their résumé.","datePosted":"2026-09-04T08:24:22.941Z","dateModified":"2026-09-04T08:24:22.941Z","hiringOrganization":{"@type":"Organization","name":"Launchcode","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"St Louis","addressRegion":"MO","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"21132db84f9baf44cf17f06e"},"url":"https://jobsearcher.com/jobs/21132db84f9baf44cf17f06e"}}