Back
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Mid-level, metro-based Azure data role with a popular title and mid experience increases competition.
Azure-focused data engineering in a finance environment makes cross-industry fit moderately sensitive.
Explicit 4-7 years plus mandatory Azure Databricks, ADF, PySpark and DevOps skills enforce strict filters.
Design, implement, and operationalize data engineering and ingestion solutions using Azure Cloud services such as Azure Data Factory, Databricks, Data Lake Gen2, and Azure SQL.
Lead migration of on-premise data warehouses to Azure cloud platforms and optimize pipelines by capacity planning and performance tuning.
Support data visualization using Power BI and develop Python-based APIs on Azure Function Apps, while ensuring adherence to organization risk and controls.
4-7 years of professional experience in Azure Data Engineering or related domain.
Proficient with Azure services: Data Factory, Data Lake Storage V2, SQL, Databricks, PySpark, Azure DevOps for CI/CD pipelines.
Graduate or Post-graduate degree preferably in Computer Science, Statistics, Mathematics, Data Science, Engineering, or related discipline.
Microsoft Azure certification is desirable but not mandatory.
Experienced in end-to-end data engineering lifecycle in an Azure cloud environment including migration, ingestion, transformation, and operationalization.
Comfortable collaborating with offshore teams and multiple stakeholders including business teams and technical leads across project lifecycle.
Familiar with modern data platform architectures such as Lakehouse/Datawarehouse and skilled in secure Azure practices like KeyVault and Managed Identities.