As a Technical Specialist Data Engineer, you will be a part of an Agile team to build healthcare applications and implement new features while adhering to the best coding development standards.
Responsibilities: -
- Design logical and physical data models using specified tools and methods.
- Define entities and source system mappings.
- Collaborate with business teams to create source-to-target mappings.
- Work on HEDIS measures and Clinical EPIC data to analyze and interpret healthcare performance metrics.
- Develop and optimize SQL queries for efficient data extraction, transformation, and analysis.
- Design and implement data models to support healthcare analytics, reporting, and downstream applications.
- Leverage Databricks for scalable data processing, data pipelines, and analytics workflows.
- Apply critical thinking and problem-solving skills to address complex data challenges and derive meaningful insights.
- Develop Power BI dashboards and reports for effective healthcare data visualization and decision-making.
- Collaborate with clinical analysts, data engineers, and business stakeholders to understand requirements and deliver data-driven solutions.
- Ensure data quality, integrity, and compliance with HIPAA, NCQA, and other healthcare regulations and best practices.
- Automate data validation and reconciliation processes to improve data consistency and accuracy.
- Support performance tuning and optimization of SQL queries and data pipelines for efficient processing.
Educational Qualifications: -
- Engineering Degree BE / BTech / BCS
- Technical certification in multiple technologies
- Certification in Azure, Databricks, or healthcare analytics-related domains
Skills: -
Mandatory Technical Skills: -
- Experience in developing and managing data pipelines in Databricks using PySpark/Spark SQL
- Strong data modeling skills to design efficient and scalable healthcare data structures
- Power BI for data visualization, dashboard creation, and report generation
- Strong understanding of healthcare data standards, including HL7, FHIR, and CCDA
- Experience in ETL processes and performance tuning for large datasets
- Familiarity with Azure Data Services (ADLS, ADF, Synapse) for Cloud-based data management
- Proficiency in data modeling tools such as Erwin
- Strong understanding of data management concepts
- Knowledge of the provider domain
- Familiarity with data integration, and ETL tools and techniques
- Excellent problem-solving and analytical skills
- Strong communication and collaboration abilities
- Knowledge of healthcare and pharmacy data
- Extensive experience in data modeling and data profiling, with a strong understanding of data management concepts.
- Hands-on experience with data modelling tools such as Erwin is highly desirable.
- Must have knowledge in healthcare. A good knowledge of the Provider workflows and Pharmacy data is essential.
- Familiarity with data integration and ETL concepts, techniques, and tools.
Good to Have Skills: -
- Experience in Python and PySpark for data transformation and analysis
- Knowledge of Confluent Kafka or event-driven architecture for real-time data processing
- Hands-on experience in NoSQL databases like MongoDB or Snowflake for scalable data storage
- Understanding of data governance frameworks and compliance in healthcare analytics
- Exposure to CI/CD pipelines and DevOps practices in Cloud environments
- Familiarity with Machine Learning concepts for predictive healthcare analytics
