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Senior, specialized Azure and Databricks leadership role reduces applicant competition.
Core data engineering skills transfer across industries, but semiconductor domain and Azure/Databricks preference increases fit sensitivity.
Explicit 12–15 years requirement plus Azure/Databricks, leadership and architecture mandates makes shortlisting highly strict.
Own end-to-end delivery of scalable, secure, AI-ready data engineering and analytics solutions across enterprise initiatives.
Lead architecture governance, solution design, engineering standards, and technical escalation for Azure Data Platform, including ADF, Databricks, and Lakehouse architectures.
Manage and mentor data engineering teams while partnering with business stakeholders to translate objectives into technical roadmaps and execution plans.
Bachelor’s degree in technology or engineering.
12-15 years of overall experience in Data & Analytics with at least 5+ years hands-on experience in Azure data engineering technologies.
Minimum 3+ years in data engineering team leadership or similar technical lead role.
Strong expertise with Azure Data Factory, Databricks, PySpark, SQL, and knowledge of Databricks Unity Catalog and CI/CD via Azure DevOps.
Experienced technical leader with a strong background in architecting scalable, secure, AI-ready data platforms in Azure environments.
Proven track record of driving delivery execution, engineering best practices, and mentoring engineering teams towards excellence.
Comfortable engaging at senior business and technical stakeholder levels to align solutions with strategic business goals.