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About the role
As a Data Engineer at Xebia, you will play a crucial role in the technical backbone of the SAC Workforce Planning implementation. Your primary responsibility will be to ensure a seamless flow of employee, position, and cost data from SuccessFactors and Databricks into SAC Analytics Cloud (SAC), with a focus on accuracy, automation, and performance. Key Responsibilities: - Partner directly with the SAC Consultant to design and optimize the Data Layer in Datasphere for the SAC Planning Model. - Develop and manage complex extraction logic from SuccessFactors to create "point-in-time" snapshots for attrition analysis and budget-vs-actual variance. - Utilize Databricks (Spark/Python) for advanced data transformations exceeding standard SAP capabilities. - Build and maintain the Business Layer and Data Layer in SAP Datasphere, creating views and models optimized for SAC consumption. - Design and monitor end-to-end data orchestration schedules to ensure automated data refresh from source systems. - Implement automated reconciliation scripts to verify data accuracy and consistency before pushing to the SAC Planning model. Qualifications Required: - Expert knowledge of SAP Datasphere including Spaces, Data Flows, and Transformation Flows. - Proficiency in Databricks/Spark, with experience in PySpark or SQL for data cleaning and enrichment. - Hands-on experience with SAP BTP and Cloud Connector for integration. - Understanding of data modeling concepts like Star Schema and Snowflake Schema for OLAP and SAC Planning. - Expertise in implementing Data Access Controls (DAC) for security and PII governance in Datasphere. (Note: Additional details about the company were not provided in the job description.) As a Data Engineer at Xebia, you will play a crucial role in the technical backbone of the SAC Workforce Planning implementation. Your primary responsibility will be to ensure a seamless flow of employee, position, and cost data from SuccessFactors and Databricks into SAC Analytics Cloud (SAC), with a focus on accuracy, automation, and performance. Key Responsibilities: - Partner directly with the SAC Consultant to design and optimize the Data Layer in Datasphere for the SAC Planning Model. - Develop and manage complex extraction logic from SuccessFactors to create "point-in-time" snapshots for attrition analysis and budget-vs-actual variance. - Utilize Databricks (Spark/Python) for advanced data transformations exceeding standard SAP capabilities. - Build and maintain the Business Layer and Data Layer in SAP Datasphere, creating views and models optimized for SAC consumption. - Design and monitor end-to-end data orchestration schedules to ensure automated data refresh from source systems. - Implement automated reconciliation scripts to verify data accuracy and consistency before pushing to the SAC Planning model. Qualifications Required: - Expert knowledge of SAP Datasphere including Spaces, Data Flows, and Transformation Flows. - Proficiency in Databricks/Spark, with experience in PySpark or SQL for data cleaning and enrichment. - Hands-on experience with SAP BTP and Cloud Connector for integration. - Understanding of data modeling concepts like Star Schema and Snowflake Schema for OLAP and SAC Planning. - Expertise in implementing Data Access Controls (DAC) for security and PII governance in Datasphere. (Note: Additional details about the company were not provided in the job description.)
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