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Multiple mid-level amplifiers: known brand, popular Data Engineer title, 5+ experience, broad Azure skillset.
Core Azure data engineering skills are transferable, but banking domain preference raises domain-specific bias.
Multiple mandatory 5+ year requirements and specific Azure Databricks/ADF/DevOps skills increase filtering.
Lead data engineering efforts on Azure platform focusing on Azure Databricks, Data Factory, and DevOps including CI/CD pipeline management and release activities.
Own design, development, and maintenance of ETL pipelines, data models, data governance, metadata, lineage, and quality frameworks.
Provide production support, collaborate with stakeholders for data solutions, and participate in architecture and design reviews.
5+ years hands-on experience with Azure Databricks, Azure Data Factory, and Azure DevOps.
Strong experience in Python and PySpark programming for data engineering tasks.
Experience with data modeling, data governance, metadata management, data profiling, and ETL development.
Work Experience Required: Not explicitly mentioned in the JD; Banking domain experience strongly desired but not mandatory.
Experienced in managing data engineering in enterprise-scale Azure environments with emphasis on DevOps and release engineering.
Capable of communicating technical details clearly to non-technical business partners and supporting testing & production support.
Familiar with large-scale banking or financial data environments and enterprise data governance practices.