Join us as a Custom Software Engineer focused on building AI-native data integration and quality platforms. You will leverage SAP BusinessObjects Data Services (BODS) with deep ETL and data management expertise, incorporating agentic AI patterns. This role moves beyond traditional ETL to create intelligent, self-optimizing data pipelines that can understand data structures, detect anomalies, suggest transformations, and accelerate data modernization.

This position emphasizes innovation by enabling data pipelines to reason about data, identify issues, and propose solutions without the need for foundational model retraining. The goal is to deliver faster data pipeline development, enhance data trust through automation, and establish scalable data integration foundations supporting analytics and enterprise decision-making.

Design and develop robust SAP BusinessObjects Data Services (BODS) ETL jobs for diverse data sources and systems, ensuring scalability and performance. Implement complex data transformations, enrichment, and harmonization logic, adhering to enterprise data architecture standards.

Build and maintain data quality rules, profiling pipelines, and governance features to ensure data accuracy, consistency, and lineage. Develop AI-powered data engineering agents capable of analyzing metadata, recommending transformations and quality rules, and auto-generating ETL scaffolding.

Implement automated validation and evaluation loops for AI behaviors and ETL artifacts, ensuring measurable quality thresholds. Optimize ETL job performance, scalability, and reliability with robust error handling and recovery mechanisms. Monitor pipeline execution, perform root cause analysis for issues, and leverage AI for diagnostics and remediation. Support modernization efforts by integrating BODS with cloud platforms and collaborating on advanced data architectures.

A Bachelor's degree or equivalent is required, with a minimum of 3 years of professional experience. Strong hands-on expertise 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 necessary. Experience in designing reliable, scalable data pipelines with a focus on performance and governance is a must. Familiarity with AI-native capabilities such as tool-augmented workflows, retrieval-grounded recommendations, evaluation loops, and safe automation boundaries is highly beneficial.