Join us as a Custom Software Engineer and spearhead the development of AI-native data integration and quality platforms. This role leverages SAP BusinessObjects Data Services (BODS) to construct intelligent, self-optimizing data pipelines. You'll move beyond traditional ETL, building solutions that can reason about data, detect anomalies, and accelerate modernization.

This position focuses on combining deep ETL and data management expertise with agentic AI patterns. The goal is to create efficient, scalable, and insightful data processing systems without requiring foundation model training from scratch.

Design and develop BODS data integration jobs for diverse systems, implementing robust batch and near real-time pipelines. Build reusable data flows and transforms, adhering to enterprise architecture standards.

Implement complex data transformation and enrichment logic using BODS features. Apply canonical data modeling to reduce complexity and duplication.

Develop data quality rules for validation, cleansing, and standardization. Build profiling and validation pipelines to ensure data completeness and accuracy.

Engineer AI-native agents capable of analyzing metadata, recommending transformations, proposing data quality rules, and auto-generating ETL mappings. Implement retrieval-grounded assistance for metadata catalogs and business rules.

Design automated validation strategies for data and AI behaviors. Establish evaluation harnesses for AI accuracy and drift detection, ensuring quality thresholds are met before releases.

A minimum of 2 years of professional experience in SAP BusinessObjects Data Services (BODS) ETL development and operations is essential.

Demonstrated expertise in data integration patterns, transformation logic, and enterprise data quality practices is required. Solid experience designing reliable, scalable data pipelines with performance and governance in mind is expected.

Familiarity with AI-native capabilities, including tool-augmented workflows and retrieval-grounded recommendations, is a significant advantage. Experience with data warehousing, analytics fundamentals, and metadata management concepts is strongly beneficial.