Join us as a Custom Software Engineer and contribute to building AI-native data integration and quality platforms. This role focuses on leveraging SAP BusinessObjects Data Services (BODS) to create intelligent, self-optimizing data pipelines. You'll move beyond traditional ETL to develop solutions that can reason about data, detect anomalies, and accelerate data modernization without training foundation models from scratch.

This position offers the opportunity to apply deep expertise in ETL, data management, and metadata with agentic AI patterns. We aim to build advanced data solutions that enhance efficiency and trust in data operations, supporting enterprise decision-making and AI initiatives.

Design and develop robust BODS data integration jobs for diverse systems, implementing both batch and near real-time data pipelines. Build reusable data flows and workflows, and craft complex transformation logic using BODS features. Implement data quality rules, profiling, and validation pipelines, ensuring data completeness, accuracy, and consistency. Develop AI-driven agents for automated transformation logic recommendations, data quality rule proposals, and ETL scaffolding generation.

Establish automated testing and validation strategies, including schema checks, reconciliation rules, and regression comparisons. Optimize ETL jobs for performance and scalability, ensuring reliable operations through effective error handling and restartability. Monitor pipeline execution and proactively address issues, utilizing AI-augmented diagnostics for root cause analysis. Support modernization by integrating BODS pipelines with cloud platforms and collaborating with cross-functional teams to deliver end-to-end data solutions.

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

Familiarity with AI-native capabilities, including tool-augmented workflows, retrieval-grounded recommendations, evaluation loops, and safe automation, is highly beneficial. Additional strengths include data warehousing fundamentals, metadata management, lineage concepts, and experience integrating ETL platforms with cloud data ecosystems. Scripting or automation skills for pipeline orchestration are also advantageous. A full-time education spanning 15 years is a prerequisite for this role.