This role focuses on building AI-native data integration and quality platforms using SAP BusinessObjects Data Services (BODS). The objective is to move beyond traditional batch ETL, creating intelligent, self-optimizing data pipelines. These pipelines will reason about data structures, detect anomalies, recommend transformations, and accelerate data modernization without requiring foundational model training.

The position emphasizes combining deep expertise in ETL, data management, and metadata with agentic AI patterns. This includes leveraging LLMs, tools, retrieval, and evaluation to enhance data processes.

Develop and operate BODS data integration jobs for diverse data types and systems. Implement robust batch and near real-time data pipelines supporting analytics, warehousing, and downstream applications. Design complex transformation logic and implement data enrichment, standardization, and harmonization. Implement data quality rules for validation, cleansing, and matching, alongside profiling and validation pipelines. Build data engineering agents for metadata analysis, transformation recommendation, and rule proposal. Design automated validation strategies and establish evaluation harnesses for AI behaviors. Optimize ETL jobs for performance, scalability, and reliability, implementing error handling and recovery mechanisms. Monitor pipelines, perform root cause analysis for issues, and utilize AI-augmented diagnostics. Support modernization initiatives by integrating BODS with cloud platforms and collaborating with data architects and analytics teams.

A minimum of 2-5 years of experience is required, with a minimum of 3 years in SAP BusinessObjects Data Services (BODS) ETL development and operations. A solid understanding of data integration patterns, transformation logic, and enterprise data quality practices is essential. Candidates should have experience designing reliable, scalable data pipelines with performance and governance in mind.

Proficiency in AI-native capabilities such as tool-augmented workflows, retrieval-grounded recommendations, evaluation loops, and safe automation boundaries is crucial. Data warehousing and analytics fundamentals, metadata management, lineage concepts, and data governance frameworks are strongly beneficial. Experience integrating ETL platforms with cloud data ecosystems and modern analytics tools, as well as scripting or automation skills, are also advantageous. A full-time education of 15 years is a mandatory requirement.