Senior Data Engineer (Turkish Language) at AmorServ

Requirements

• - In-depth experience in leading transformations of cloud data platforms.
• - Demonstrated success in deploying large-scale data and analytical solutions in cloud settings.
• - Hands-on proficiency in comprehensive data pipeline creation on platforms such as Azure Data Factory.
• - Deep understanding of contemporary data architectures (Data Lake, Warehouse and Lakehouse).
• - Solid comprehension of data architecture and data modelling techniques.
• - Efficient management of data pipelines from a cost perspective.
• - Acquaintance with CI/CD-driven data pipeline and infrastructure.
• - Agile delivery methodology using Scrum and Kanban frameworks.
• - Ability to independently scope, estimate, and deliver tasks on time within an agile framework.
• - Proactive in enhancing data processes.
• - Proficient in Turkish and English Languages.

Responsibilities

• - Highly skilled in data assessment, data modelling and designing ETL processes.
• - Excellent understanding of the concepts of data lake and Synapse on Azure.
• - Expert level knowledge of building data pipelines with Microsoft Azure Data Factory
• - Expert at T-SQL, Python and Pyspark, with experience in performance tuning and dealing with large data volumes.
• - Extensive experience of developing end-to-end reporting and analytics solutions with technologies like Microsoft Power BI, SSIS, SSRS and SSAS, Power Query / DAX.
• - Good understanding of key concepts of enterprise big data architecture.
• - Experience and good understanding of the full software development lifecycle, including the use of Azure DevOps for continuous integration and deployment CICD.
• - Good level of understanding the differences in the approach for building self-service reporting solutions including the choice of data sources and ETL approach.
• - Understanding security model designs within the data estate.
• - Business knowledge in consumer goods manufacturing would be desirable.
• - Understanding engineering processes for developing APIs.
• - Experience of Agile working practices is essential.
• - Understanding of data quality approaches is essential.