Join our team as a Custom Software Engineer focused on AI-native data integration. You will leverage SAP BusinessObjects Data Services (BODS) to build intelligent, self-optimizing data pipelines. This role moves beyond traditional ETL, incorporating agentic AI patterns to reason about data structures, detect anomalies, and accelerate data modernization without requiring foundational model training.

This position is integral to creating AI-native data integration and data quality platforms. You will combine deep ETL, data management, and metadata expertise with cutting-edge AI techniques. The goal is to develop solutions that are scalable, efficient, and intelligent, driving significant value in data processing.

Design and develop robust BODS data integration jobs for diverse systems. Implement scalable batch and near real-time data pipelines for analytics and reporting. Develop complex transformation logic and enforce data quality rules for validation and cleansing. Build AI-driven data engineering agents capable of analyzing metadata and recommending transformations. Implement automated validation strategies and evaluation harnesses for AI behaviors. Optimize ETL jobs for performance, scalability, and reliability. Monitor pipeline execution and perform root cause analysis for any issues.

Collaborate with cross-functional teams to support modernization initiatives and transition legacy ETL logic. Enhance enterprise data architecture standards and ensure governance requirements are met. Proactively identify bottlenecks and data degradation patterns. Support the development of end-to-end data solutions that meet business needs.

A minimum of 3 years of experience in SAP BusinessObjects Data Services (BODS) ETL development and operations is essential. Solid understanding of data integration patterns, transformation logic, and enterprise data quality practices is required. Experience designing reliable, scalable data pipelines with a focus on performance and governance is crucial. Familiarity with AI-native capabilities, including tool-augmented workflows and retrieval-grounded recommendations, is expected.

Additionally, a strong grasp of data warehousing fundamentals, metadata management, and data governance frameworks is highly beneficial. Experience integrating ETL platforms with cloud data ecosystems and modern analytics tools, along with scripting or automation skills, will further strengthen your candidacy. This role requires 15 years of full-time education.