This role focuses on building AI-native data integration and data quality platforms using SAP BusinessObjects Data Services (BODS). The position requires deep expertise in ETL, data management, and metadata combined with agentic AI patterns. The objective is to move beyond traditional batch ETL to intelligent, self-optimizing data pipelines capable of reasoning about data, detecting anomalies, and accelerating data modernization.

Key responsibilities include designing and developing enterprise data integration jobs, implementing complex data transformations, and ensuring data quality through profiling and governance. A significant part of the role involves building AI data engineering agents that can analyze metadata, propose transformations, and automate ETL scaffolding. The position also emphasizes robust testing, validation, performance optimization, and proactive monitoring of data pipelines. Collaboration with data architects and analytics teams is crucial for delivering end-to-end data solutions.

Design and develop BODS data integration jobs for diverse systems, implementing batch and near real-time data pipelines. Build reusable data flows and workflows aligned with enterprise architecture standards. Implement complex transformation logic and data enrichment across multiple source systems. Apply canonical data modeling practices to minimize redundancy and complexity. Establish data quality rules for validation, cleansing, and standardization. Build profiling pipelines to assess data completeness, accuracy, and consistency. Develop data engineering agents for metadata analysis, transformation logic recommendation, and AI-driven ETL scaffolding. Implement retrieval-grounded assistance using metadata catalogs and business rules. Design automated validation strategies including schema checks, row counts, and reconciliation rules. Optimize ETL jobs for performance and scalability, implementing error handling and recovery mechanisms. Monitor job execution and data quality metrics, performing root cause analysis for failures. Support modernization initiatives by integrating BODS pipelines with cloud data platforms.

A minimum of 3 years of hands-on experience in SAP BusinessObjects Data Services (BODS) ETL development and operations is required. Solid understanding of data integration patterns, transformation logic, and enterprise data quality practices is essential. Demonstrated experience in designing reliable, scalable data pipelines with a focus on performance and governance. Familiarity with AI-native capabilities such as tool-augmented workflows, retrieval-grounded recommendations, and evaluation loops is expected. Knowledge of data warehousing, analytics fundamentals, metadata management, and data governance frameworks is strongly beneficial. Experience integrating ETL platforms with cloud data ecosystems and modern analytics tools is advantageous. Scripting or automation skills to support pipeline orchestration and operational tooling are desirable.