Join us as a Custom Software Engineer to build AI-native data integration and data quality platforms. You will leverage deep expertise in ETL, data management, and metadata, combined with agentic AI patterns. This role transcends traditional batch ETL, focusing on intelligent, self-optimizing data pipelines that can analyze data structures, identify anomalies, suggest transformations, and accelerate data modernization without the need for training foundational models from scratch.

Design and develop robust ETL jobs using SAP BusinessObjects Data Services (BODS) for diverse systems. Implement efficient data pipelines for analytics, reporting, and data warehousing. Create reusable data flows and transforms aligned with enterprise architecture standards. Design complex transformation logic, implement data enrichment, and apply canonical data modeling. Build data quality rules, profiling pipelines, and support governance requirements.

Develop AI-native data engineering agents capable of analyzing metadata, recommending transformations, and proposing data quality rules. Implement retrieval-grounded assistance using metadata catalogs and business rules. Enable conversational exploration of data pipelines with auditable outputs. Design automated validation strategies and establish evaluation harnesses for AI behaviors.

Optimize ETL jobs for performance and scalability, ensuring error handling, restartability, and idempotency. Monitor pipelines for bottlenecks and implement proactive measures. Perform root cause analysis for issues and leverage AI-augmented diagnostics. Support modernization initiatives by integrating BODS with cloud platforms and collaborating with cross-functional teams.

A minimum of 3 years of hands-on experience with 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 performance and governance in mind is also necessary.

This role demands AI-native capabilities, including tool-augmented workflows, retrieval-grounded recommendations, evaluation loops, and safe automation boundaries. A foundational understanding of data warehousing, analytics, metadata management, and data governance is strongly beneficial. Experience with cloud data ecosystems, modern analytics tools, and scripting/automation skills is a plus.