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Multiple amplifiers: common data engineer title, 5+ years mid-level, metro locations, broad Databricks/Fabric skills.
Core data engineering skills transferable, but Azure/Databricks expertise increases industry specificity.
Explicit 5+ years and mandatory Databricks, Spark, Fabric, CI/CD and data quality requirements.
Design, develop, and maintain scalable data platforms and pipelines using Databricks, Microsoft Fabric, SQL, Spark, and Delta Lake.
Build automation frameworks for data validation, testing, monitoring, and deployment to ensure data quality and operational reliability.
Implement security controls, governance standards, and optimize performance and cost efficiency across enterprise data environments.
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 in building data pipelines, ETL/ELT solutions, lakehouse architectures, and data integration frameworks.
Proven skills in automation frameworks, CI/CD pipelines using Azure DevOps or GitHub, and data quality/governance practices.
Experienced in end-to-end data platform engineering including design, development, testing, deployment, and support of large-scale solutions.
Skilled at collaborating with cross-functional teams and managing stakeholder expectations to deliver enterprise-grade data solutions.
Capable of leading technical discussions, implementing engineering best practices, and mentoring junior engineers.