Senior Databricks Data Engineer

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.

• 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.