This role focuses on building AI-native data integration and data quality platforms using SAP BusinessObjects Data Services (BODS). You will leverage deep ETL, data management, and metadata expertise combined with agentic AI patterns. The goal is to create intelligent, self-optimizing data pipelines that can analyze data structures, detect anomalies, and accelerate data modernization.

This position is not centered on training foundation AI models or manual ETL development without proper controls. Instead, it delivers value through faster pipeline development, higher data trust, and scalable data integration foundations.

Design and develop BODS data integration jobs for various systems, implementing robust batch and near real-time data pipelines. You will build reusable data flows and design complex transformation logic. The role involves implementing data quality rules, profiling, and validation pipelines to ensure data accuracy and completeness. Additionally, you will build AI-native data engineering agents capable of analyzing metadata, recommending transformations, and auto-generating ETL scaffolding. Developing automated validation strategies and optimizing ETL jobs for performance, scalability, and reliability are also key responsibilities.

This includes monitoring job execution, performing root cause analysis for issues, and supporting modernization initiatives by integrating BODS pipelines with cloud platforms. You will collaborate with data architects and analytics teams to deliver end-to-end data solutions.

A minimum of 2-5 years of experience is required for this full-time position. Strong hands-on expertise in SAP BusinessObjects Data Services (BODS) ETL development and operations is essential. A solid understanding of data integration patterns, transformation logic, and enterprise data quality practices is also necessary. Experience designing reliable, scalable data pipelines with performance and governance in mind is crucial.

AI-native capabilities, including tool-augmented workflows, retrieval-grounded recommendations, evaluation loops, and safe automation boundaries, are expected. Beneficial skills include data warehousing and analytics fundamentals, metadata management, lineage concepts, data governance frameworks, and experience integrating ETL platforms with cloud data ecosystems. Scripting or automation skills for pipeline orchestration are also strongly beneficial.