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The Databricks Data Engineer will play a critical role in accelerating data modernization efforts. Their expertise will support timely report conversion, improve data pipeline efficiency, and help ensure data integrity and accessibility across the organization. Databricks platform expertise (workspace management, clusters, job scheduling). Spark (PySpark/Scala) proficiency. SQL optimization and advanced querying skills. Azure Data Factory (ADF) or equivalent ETL pipeline experience Data warehousing concepts and architecture (Delta Lake). Python scripting for data manipulation and automation. Familiarity with Azure Cloud environment (Blob Storage, ADLS). Knowledge of data governance, data security, and compliance. Experience Required: 3+ years hands-on experience with Databricks, including cluster management and optimization. 3+ years of Delta Lake architecture and implementation. 5+ years background in PySpark or Scala for data engineering tasks. 5+ years of demonstrated success building efficient ETL pipelines, using Azure Data Factory (ADF) or similar. 5+ years of migrating legacy reports to modern cloud analytics platforms. 5+ years of strong SQL skills with expertise in data modeling and performance tuning. 5+ years of BI tools such as Power BI or Tableau. Education: Bachelor's degree in Computer Science, Information Systems, Data Science, or related technical field. Relevant industry certifications in Databricks, Azure Cloud, Data Engineering, or similar fields. Databricks Certified Data Engineer Associate (No more than 12 months expired) All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender, identity, national origin, disability, or protected veteran status. #J-18808-Ljbffr