Lead design and implementation of Databricks Lakehouse data platforms for healthcare. Build scalable ETL/ELT pipelines with PySpark/SQL, enforce data governance, security (HIPAA/PHI/PII), integrate clinical and claims sources, optimize performance and costs, and mentor engineering teams.
We are looking for a Senior Databricks Data Engineer with strong expertise in Databricks, cloud data architecture, data engineering, and healthcare data. The ideal candidate will be responsible for designing scalable, secure, and high-performance data platforms supporting healthcare analytics and data integration.
The candidate should have strong knowledge of modern data architecture, Lakehouse architecture, SQL, Python, data governance, and healthcare domain workflows.
Responsibilities- Design and lead Databricks Lakehouse architecture for large-scale healthcare data platforms.
- Define scalable data architectures for batch and real-time healthcare data processing.
- Design data ingestion, transformation, storage, and consumption patterns using Databricks.
- Develop architecture using Delta Lake, Unity Catalog, Databricks Workflows, and Delta Live Tables/Lakeflow.
- Integrate data from multiple healthcare sources, including clinical, claims, eligibility, provider, and patient systems.
- Design robust ETL/ELT pipelines using PySpark, SQL, and Databricks.
- Establish data quality, validation, reconciliation, lineage, and governance frameworks.
- Design secure healthcare data solutions while considering HIPAA and PHI/PII protection requirements.
- Work with cloud platforms such as Azure, AWS, or GCP to build scalable data solutions.
- Collaborate with data engineers, data scientists, analysts, product owners, and business stakeholders.
- Define technical standards, architectural patterns, and best practices for Databricks development.
- Lead performance optimization and cost-management initiatives across the Databricks platform.
- Provide technical leadership and mentoring to data engineering teams.
- Participate in Agile ceremonies and contribute to technical planning and roadmap discussions.
- Strong hands-on experience with Databricks.
- Expertise in Lakehouse architecture and Delta Lake.
- Strong PySpark and SQL skills.
- Experience with Unity Catalog and data governance.
- Experience designing enterprise-scale data platforms.
- Strong knowledge of ETL/ELT and data modeling.
- Experience with cloud platforms such as Azure, AWS, or GCP.
- Experience with data integration and orchestration tools.
- Knowledge of CI/CD, Git, and DevOps practices.
- Strong understanding of data security and access control.
- Experience with Agile/Scrum methodologies.
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