Embark on a journey to build AI-native data integration and quality platforms using SAP BusinessObjects Data Services (BODS). This role champions the evolution from traditional batch ETL to intelligent, self-optimizing data pipelines. You will leverage deep expertise in ETL, data management, and metadata, combined with agentic AI patterns, to drive data modernization without the need for extensive foundational model training.

This position focuses on creating innovative solutions that can intelligently reason about data structures, identify anomalies, suggest transformations, and accelerate your organization's data initiatives. It’s an opportunity to be at the forefront of AI-powered data engineering.

Design and implement robust BODS data integration jobs for diverse data sources, ensuring reliable batch and near real-time pipelines for analytics and warehousing. Develop complex transformation logic and apply canonical data modeling for efficient data enrichment and standardization. Build data quality rules, profiling pipelines, and lineage-aware jobs to ensure data integrity and support governance. Create AI agents capable of analyzing metadata, recommending transformations, proposing quality rules, and auto-generating ETL scaffolding. Implement automated validation strategies and evaluation harnesses for AI behaviors, ensuring measurable quality thresholds for all releases. Optimize ETL jobs for performance and scalability, implementing robust error handling and recovery mechanisms. Monitor pipeline execution, conduct root cause analysis for failures, and utilize AI-augmented diagnostics for efficient incident response. Support modernization efforts by integrating BODS with cloud platforms and collaborating on evolving data architectures.

A minimum of 3 years of dedicated experience in SAP BusinessObjects Data Services (BODS) ETL development and operations is essential. Demonstrated proficiency in data integration patterns, transformation logic, and enterprise data quality practices is required. Proven experience in designing reliable, scalable data pipelines with a strong emphasis on performance and governance is a must. Familiarity with AI-native capabilities, including tool-augmented workflows, retrieval-grounded recommendations, evaluation loops, and safe automation boundaries, is crucial. Additionally, a foundational understanding of data warehousing, analytics fundamentals, metadata management, and data governance frameworks will be highly beneficial for success in this role.