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Senior level and Databricks/Azure niche reduce applicants despite metro location.
Transferable data engineering skills, but enterprise EDW and Azure/Databricks focus increases domain specificity.
Mandatory 10–12 years plus required Databricks, Azure, PySpark and EDW skills makes filters stringent.
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Lead design and implementation of scalable Enterprise Data Warehouse and Data Lakehouse solutions on Azure using Databricks and Delta Lake.
Build and optimize high-volume ETL/ELT pipelines with Python and PySpark in Azure Databricks, monitor and tune performance for cost and efficiency.
Lead migration of legacy EDW workloads to modern cloud stack and mentor a team of data engineers enforcing best practices and code quality.
10-12 years of experience in Data Engineering, Business Intelligence, or Enterprise Data Warehousing.
Strong expertise with Azure Data ecosystem including Azure Data Factory (ADF), Azure Data Lake Storage Gen2, Azure SQL/Synapse.
4+ years hands-on experience with Databricks, Apache Spark (PySpark & Spark SQL), and Delta Lake.
Proficient in advanced SQL, Python programming (including Pandas, OOP), and data modeling concepts; certifications in Azure Data Engineer Associate (DP-203) or Databricks Data Engineer are preferred.
Experienced in designing and modernizing enterprise data integration frameworks and workflows with a focus on Azure and Databricks technologies.
Capable of leading technical teams with responsibility for mentoring, code reviews, and enforcing software engineering best practices such as CI/CD and automated testing.
Skilled at collaborating with enterprise architects and business stakeholders to align data infrastructure with strategic business goals and compliance standards.