Data Engineer

JD - Data Engineer (Healthcare)

 Key Responsibilities:

• Design, develop, and optimize scalable data pipelines using Azure Data Factory, Databricks, and Kafka.

• Architect and implement robust data warehousing solutions using Snowflake and other cloud-native technologies.

• Collaborate with business stakeholders and advisory consultants to understand data requirements and translate them into technical solutions.

• Ensure data quality, integrity, and security across all data platforms.

• Perform advanced data transformations, cleansing, and enrichment to support analytics and reporting needs.

• Monitor and troubleshoot data pipeline performance and reliability.

• Document technical designs, processes, and best practices.

• Design and implement healthcare-specific data models including payer modules.

• Collaborate with domain experts to develop logical, and physical data models.

• Utilize data modeling tools to create and maintain healthcare data models aligned with business requirements.

Required Qualifications:

• 7+ years of hands-on experience in data engineering and cloud platforms.

• Strong expertise in Azure Data Factory (ADF), Databricks (PySpark, Delta Lake), Apache Kafka, Snowflake.

• Proven experience in data warehousing, ETL/ELT design, and data modeling.

• Solid understanding of cloud architecture, data governance, and security best practices.

• Experience working in consulting environments with direct client interaction.

• Strong experience in healthcare data modeling, especially in payer modules.

• Proficiency in data modeling tools such as ER/Studio, Erwin Data Modeler, or similar.

• Demonstrated expertise in conceptual, logical, and physical data modeling practices.

Preferred Qualifications:

• Experience with CI/CD pipelines and DevOps practices in data engineering.

• Knowledge of healthcare data and regulatory compliance (HIPAA, PHI) is a plus.

• Certifications in Azure, Databricks, or Snowflake are highly desirable.

📄 BGV Mandatory