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Niche Databricks/PySpark skillset reduces competition despite metro location.
Platform-specific Databricks expertise reduces cross-industry portability, while general data engineering skills remain somewhat transferable.
Many mandatory Databricks, Delta Lake, DLT and PySpark skills imply strict technical and platform-specific filters.
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Design, develop, and optimize end-to-end data pipelines and Lakehouse architectures using Databricks, PySpark, and SQL.
Implement and manage Delta Lake, Delta Live Tables, and real-time data processing with Structured Streaming and Auto Loader.
Establish data governance, security, and performance optimizations using Unity Catalog and Databricks Workflows, collaborating with cross-functional teams.
Strong expertise in Databricks platform, PySpark, Delta Lake, Delta Live Tables, Unity Catalog, Structured Streaming, Auto Loader, and SQL.
Experience building batch and real-time data pipelines and Lakehouse architectures with data modeling skills.
Bachelor's or Master's degree in Computer Science, Data Engineering, Information Technology, or related discipline.
Work Experience Required: Not explicitly mentioned in the JD.
Proven hands-on experience with enterprise-scale Lakehouse architectures using Databricks and related technologies.
Strong understanding of data governance, security, compliance, and scalable data engineering best practices.
Experience delivering both batch and real-time cloud-based data pipelines in agile, collaborative environments.