This role involves building advanced, AI-native data integration and data quality platforms utilizing SAP BusinessObjects Data Services (BODS). You will leverage deep expertise in ETL, data management, and metadata, combined with agentic AI patterns. The focus is on creating intelligent, self-optimizing data pipelines that can reason about data structures, detect anomalies, and accelerate data modernization, without the need for training foundation models from scratch.

This position is about evolving beyond traditional batch ETL into a more sophisticated, AI-driven approach to data management. You will contribute to developing solutions that enhance data trust and efficiency.

Design, develop, and maintain BODS data integration jobs for diverse systems, implementing robust batch and near real-time data pipelines. Craft complex transformation logic and apply canonical data modeling practices. Implement data quality rules, profiling, and validation pipelines to ensure data integrity. Develop AI agents for analyzing metadata, recommending transformations, and proposing data quality rules. Implement retrieval-augmented assistance for verifiable recommendations and enable conversational exploration of data pipelines.

Optimize ETL jobs for peak performance and scalability, ensuring reliable operations through robust error handling and recovery mechanisms. Monitor pipeline execution, identify bottlenecks, and perform root cause analysis for issues. Support modernization initiatives by integrating BODS with cloud platforms and collaborate with cross-functional teams to deliver end-to-end data solutions.

A strong foundation in SAP BusinessObjects Data Services (BODS) ETL development and operations is essential, complemented by a solid understanding of data integration patterns, transformation logic, and enterprise data quality practices. Experience in designing reliable, scalable data pipelines with a keen focus on performance and governance is required. Familiarity with AI native capabilities, including tool-augmented workflows, retrieval-grounded recommendations, and evaluation loops, is also a key requirement.

Additionally, beneficial skills include a grasp of data warehousing and analytics fundamentals, metadata management concepts, data lineage, and data governance frameworks. Experience integrating ETL platforms with cloud data ecosystems and modern analytics tools, along with scripting or automation skills for pipeline orchestration, will be highly advantageous.