Required Skills & Experience
5-7+ years of Data Engineering experience building large-scale data platforms Strong hands-on experience with Azure Databricks, Delta Lake, and Azure Data Lake Storage (ADLS) Advanced PySpark development experience, including DataFrames, transformations, joins, aggregations, and performance tuning Experience developing and maintaining Delta Live Tables (DLT) pipelines Strong SQL background with experience supporting legacy data warehouse environments Experience migrating SQL-based pipelines to modern Python/PySpark architectures Experience with Infrastructure as Code (IaC) and Pipeline as Code methodologies Proficiency with YAML, JSON, and configuration-driven development patterns Experience automating CI/CD deployments using DevOps pipelines Strong understanding of Medallion Architecture (Bronze, Silver, Gold) Experience building ingestion, transformation, and curation pipelines across Bronze, Silver, and Gold layers Experience troubleshooting, monitoring, and supporting production data pipelines Familiarity with Databricks Asset Bundles, Unity Catalog, and Delta Lake preferred Healthcare industry experience preferred Medicare, payer, member enrollment, or claims data experience strongly preferred Understanding of healthcare data quality, PHI/PII handling, and regulatory requirements preferred
Job Description
Design, develop, and deploy Azure Databricks data pipelines using PySpark and Delta Live Tables Modernize legacy on-prem SQL pipelines into scalable cloud-native Databricks solutions Build configuration-driven data pipelines using YAML, JSON, and reusable framework patterns Develop and maintain CI/CD deployment processes for data engineering workflows Implement and support Medallion Architecture pipelines across Bronze, Silver, and Gold layers Partner with upstream and downstream teams to ensure data quality, reliability, and scalability Troubleshoot and resolve production pipeline issues across Databricks and legacy SQL environments Enhance monitoring, observability, and operational support processes Support Medicare and payer-focused initiatives involving member, claims, and operational datasets Follow established architecture standards and engineering best practices to ensure platform consistency and maintainability
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