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Data Engineer
Key Responsibilities
- Design, build, and maintain data pipelines that extract, transform, and load data from source systems into reporting and analytics environments.
- Develop and maintain the data warehouse / data mart structure to support operational, clinical, financial, and population health reporting.
- Integrate data from multiple systems such as:
- HIS / EMR
- ERP / Finance
- HR systems
- Laboratory / Radiology / Pharmacy systems
- Manual and external data sources
- Clean, standardize, and validate data to improve data quality, consistency, and usability .
- Build and maintain reusable datasets for Power BI dashboards , KPI tracking, and automated reporting.
- Work with analysts and business stakeholders to translate reporting needs into structured data models.
- Support the development of:
- patient registries
- physician registries
- disease registries
- performance and KPI datasets
- Monitor pipeline performance, troubleshoot failures, and ensure timely data refresh.
- Document data sources, logic, definitions, and transformations clearly.
- Participate in data governance efforts, including master data standardization, metric definitions, and access controls.
- Ensure compliance with organizational policies related to data privacy, security, and confidentiality .
Required Qualifications
- Bachelor’s degree in:
- Computer Science
- Data Engineering
- Information Systems
- Software Engineering
- or related field
- Proven experience in data engineering, ETL development, or database development
- Strong SQL skills
- Experience with data integration and transformation tools
- Experience working with relational databases and large datasets
- Experience supporting BI/reporting environments
Preferred Qualifications
- Experience in healthcare, hospital, or EMR/HIS data environments
- Experience with:
- Microsoft SQL Server
- Azure Data Factory
- SSIS
- Python
- Power BI backend support
- Familiarity with healthcare data domains such as visits, billing, orders, labs, diagnoses, procedures, and registries
- Understanding of data warehouse concepts such as:
- star schema
- fact and dimension tables
- data modeling
- data lineage
Key Skills
- Strong SQL and database skills
- ETL / ELT pipeline development
- Data modeling
- Problem-solving and troubleshooting
- Attention to detail
- Documentation and organization
- Ability to work with both technical and non-technical stakeholders
- Understanding of data quality and governance principles
Core Deliverables
- Reliable automated data pipelines
- Clean and validated reporting datasets
- Structured data models for dashboards and KPIs
- Better accessibility of data across departments
- Reduced manual reporting effort
- Improved readiness for enterprise analytics and value-based care initiatives
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