Senior Data Engineer, Healthcare ETL & Dashboards

Machine Learning Technologies, LLC; Austin (TX); On-site; USD 110,000 - 160,000; Full time

Job summary

Machine Learning Technologies, LLC in Austin, TX, seeks a senior data integration engineer to design, test, and maintain data pipelines in healthcare environments. You will work with SAS, Python or R, develop dashboards, and automate workflows to improve data interoperability and operational efficiency.

You will gather business requirements, translate them into technical solutions, validate data quality, and collaborate with epidemiologists, program staff, and other stakeholders.

Qualifications

• 8+ years in SAS/Python/R development, testing, and maintenance.
• 8+ years in system integration, dashboards, and automation.
• 8+ years working with healthcare or administrative datasets.
• Strong knowledge of databases and SQL querying.
• 8+ years translating business requirements into technical solutions.
• 8+ years validating data integration workflows.
• 8+ years documenting code, specs, and SOPs.
• Strong analytical and communication skills across stakeholders.
• Experience collaborating with cross-functional teams.

Responsibilities

• Develop, test, maintain, and troubleshoot programs using SAS, Python, or R.
• Build data integrations and dashboards to improve data interoperability.
• Automate data processing workflows to boost efficiency.
• Collaborate with epidemiologists, program staff, and technical stakeholders.
• Document workflows and technical specifications for data pipelines.

Skills

SAS/Python/R

Data integration

Dashboard development

Healthcare data

SQL

ETL processes

Tools

HL7

FHIR

XML

Job description

Machine Learning Technologies, LLC in Austin, TX, seeks a senior data integration engineer to design, test, and maintain data pipelines in healthcare environments. You will work with SAS, Python or R, develop dashboards, and automate workflows to improve data interoperability and operational efficiency.

You will gather business requirements, translate them into technical solutions, validate data quality, and collaborate with epidemiologists, program staff, and other stakeholders.