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Generalist mid-level Data Engineer title, metro locations, and 3-6 years amplify applicant competition.
Core data engineering skills are transferable, but Databricks/Fabric platform expertise increases domain specificity.
Explicit 5+ years and mandatory Databricks, Spark, Fabric, SQL, and CI/CD create high technical filters.
Design, develop, and maintain scalable data engineering solutions involving Databricks, Microsoft Fabric, Spark, and Delta Lake.
Build and automate data validation, testing, monitoring, and deployment frameworks to ensure data quality and governance.
Lead full lifecycle data engineering activities including CI/CD deployment automation, performance optimization, and adherence to security controls like Row-Level Security.
5+ years of experience in Data Engineering, Analytics Engineering, or Data Platform Development.
Strong hands-on expertise with Databricks, Apache Spark (PySpark), SQL, Microsoft Fabric, and Delta Lake.
Experience implementing CI/CD pipelines using Azure DevOps, GitHub, or equivalent tools.
Bachelor's Degree in a relevant field.
Experienced in designing and maintaining lakehouse architectures and analytical data warehouses within Microsoft Fabric ecosystem.
Skillful in automation frameworks for data quality validation and orchestration using Databricks workflows and jobs.
Capable of driving engineering best practices, leading technical discussions, and collaborating with cross-functional teams for enterprise-grade data platform delivery.