Develop and enhance custom software solutions, coding components across various systems and applications. This role leverages modern frameworks and agile methodologies to deliver scalable, high-performing solutions tailored to specific business requirements. You will be instrumental in building AI-native data integration and quality platforms using SAP BusinessObjects Data Services (BODS), combining deep ETL and data management expertise with advanced AI patterns.

The focus is on advancing beyond traditional batch ETL to create intelligent, self-optimizing data pipelines. These pipelines will possess the ability to reason about data structures, detect anomalies, recommend transformations, and accelerate data modernization without the need for training foundation models from scratch. This is an opportunity to shape the future of data engineering.

Design and operate BODS data integration jobs for diverse data types and systems, implementing robust batch and near real-time pipelines. Develop complex transformation logic, data enrichment, and standardization using BODS features. Implement data quality rules, profiling, and validation pipelines to ensure data completeness, accuracy, and consistency. Build AI-native data engineering agents capable of analyzing metadata, recommending transformations, and proposing data quality rules. Design automated validation strategies and evaluation harnesses for AI behaviors. Optimize ETL jobs for performance and scalability, ensuring reliable operations through error handling and recovery mechanisms. Monitor pipelines, perform root cause analysis for issues, and leverage AI for diagnostics. Support modernization initiatives and collaborate with cross-functional teams to deliver end-to-end data solutions.

A minimum of 2-5 years of experience is required, with a mandatory 15 years of full-time education. 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 crucial. Experience in designing reliable, scalable data pipelines with a focus on performance and governance is expected. Proficiency in AI-native capabilities such as tool-augmented workflows, retrieval-grounded recommendations, evaluation loops, and safe automation boundaries is required. Beneficial skills include data warehousing, analytics fundamentals, metadata management, lineage concepts, data governance frameworks, cloud data ecosystem integration, and scripting/automation.