Develop innovative, AI-native data integration and quality platforms using SAP BusinessObjects Data Services (BODS). This role pushes beyond traditional ETL to create intelligent, self-optimizing data pipelines. You'll leverage deep expertise in ETL, data management, and metadata combined with agentic AI patterns to build solutions that understand data structures, detect anomalies, and automate modernization without requiring foundation model training.

This position focuses on creating intelligent data pipelines capable of reasoning about data, identifying issues, and recommending transformations. The goal is to accelerate data modernization efforts and enhance data quality through advanced AI-driven techniques within the BODS framework.

Design and develop BODS data integration jobs for diverse systems, implementing robust batch and near real-time data pipelines. Create reusable data flows and transformations aligned with enterprise architecture. Implement complex data transformation and enrichment logic using BODS features. Build data quality rules for validation, cleansing, and standardization, alongside profiling pipelines for data assessment. Develop AI agents for metadata analysis, transformation recommendation, and automated ETL scaffolding.

Implement retrieval-augmented assistance using metadata catalogs and business rules. Enable conversational data pipeline exploration. Design automated validation strategies, including schema checks and reconciliation rules. Establish evaluation harnesses for AI behaviors and ensure measurable quality thresholds for releases. Optimize ETL jobs for performance and scalability, implementing robust error handling and recovery mechanisms. Monitor pipelines, analyze root causes of failures, and utilize AI for diagnostics.

A minimum of 3 years of experience is essential. Proficiency in SAP BusinessObjects Data Services (BODS) for ETL development and operations is a must. A solid understanding of data integration patterns, transformation logic, and enterprise data quality practices is required. Experience in designing scalable data pipelines with performance and governance in mind is crucial.

Familiarity with tool-augmented workflows, retrieval-grounded recommendations, evaluation loops, and safe automation boundaries is highly beneficial. Knowledge of data warehousing, analytics fundamentals, metadata management, lineage concepts, and data governance frameworks is strongly recommended. Experience integrating ETL platforms with cloud data ecosystems and modern analytics tools is a plus. Scripting or automation skills for pipeline orchestration are also advantageous.