Data Integration Engineer - Healthcare Data Infrastructure

Qualified Health is a company dedicated to transforming healthcare through innovative data solutions. They are seeking a Data Integration Engineer to design and build data pipelines that convert raw healthcare data into production-ready datasets for their AI platform, collaborating closely with a Data Integration Manager.

Responsibilities:

• Design and build ETL pipelines using PySpark, SQL, and Azure data services to process healthcare data from multiple source systems
• Execute data extraction and transformation operations on complex healthcare datasets, ensuring accuracy and compliance with established standards
• Develop data quality validation frameworks to identify and resolve issues during integration, QC, and backtesting phases
• Troubleshoot technical issues including data schema mismatches, transformation logic errors, and performance bottlenecks
• Build reusable data components and standardized integration patterns that accelerate future implementations
• Optimize pipeline performance for large-scale healthcare datasets, ensuring efficient processing and resource utilization
• Implement data validation rules specific to healthcare contexts (e.g., clinical code validation, temporal logic checks, referential integrity)
• Write and maintain technical documentation for data pipelines, transformations, and integration patterns
• Support production deployments by coordinating with infrastructure teams and conducting final testing
• Partner with Data Integration Manager to translate partner requirements into technical specifications
• Participate in technical discussions with partner IT teams to understand data schemas, access methods, and integration constraints
• Provide technical guidance on data mapping specifications and transformation approaches
• Identify data quality issues and work with Manager to coordinate resolution with partners
• Share technical findings from QC and backtesting with Manager to inform partner conversations
• Contribute to continuous improvement of tools, processes, and technical standards

Requirements:

• 5+ years of experience in data analytics, data engineering, or solution delivery roles, with demonstrated expertise in data integration and ETL processes
• Strong analytical toolkit with proficiency in: PySpark for distributed data processing, Advanced SQL for data querying and transformation, Excel for data analysis and reporting
• Production ETL experience: Track record of building and maintaining production-grade data pipelines with proper error handling and monitoring
• Data quality focus: Experience implementing validation frameworks and troubleshooting data quality issues
• Healthcare data experience: Prior work with healthcare datasets (EHR, claims, clinical, lab data)
• Problem-solving mindset: Ability to independently diagnose and resolve complex technical issues
• Attention to detail: Commitment to accuracy, testing, and delivering reliable solutions
• Collaborative working style: Comfortable partnering with non-technical colleagues and adapting to feedback
• Bachelor's degree in Computer Science, Engineering, Data Science, Mathematics, or related technical field
• Epic Clarity experience: Direct work with Epic's relational database structure and clinical data models
• Healthcare data standards knowledge: Understanding of FHIR, HL7v2, DICOM, LOINC, SNOMED, ICD-10
• Azure cloud platform: Hands-on experience with Azure Databricks, Data Factory, Blob Storage, Delta Lake
• Healthcare compliance awareness: Understanding of HIPAA requirements and healthcare data security best practices
• Data warehouse/lakehouse experience: Familiarity with dimensional modeling and modern data architecture patterns
• DevOps practices: Experience with Git, CI/CD pipelines, and infrastructure-as-code
• Performance tuning: Proven ability to optimize complex data transformations for scale
• LIMS/PACS experience: Prior work integrating laboratory or imaging systems data
• Multiple data format fluency: Experience with JSON, XML, Parquet, CSV, and other healthcare interchange formats