Drive innovation in data integration and quality by building AI-native platforms using SAP BusinessObjects Data Services (BODS). This role leverages deep expertise in ETL, data management, and metadata combined with agentic AI patterns. Focus on creating intelligent, self-optimizing data pipelines that can analyze data structures, detect anomalies, recommend transformations, and accelerate data modernization, moving beyond traditional batch ETL processes without requiring foundational model training.

This position offers the opportunity to contribute to Accenture's technology solutions, empowering change and enabling people through innovative software engineering. It's a chance to be a catalyst in the technology landscape, working with cutting-edge technologies to help clients reinvent their operations and positively impact the world.

Design, develop, and operate BODS data integration jobs for diverse data sources, implementing robust batch and near real-time pipelines for analytics and data warehousing. Build reusable data flows and design complex transformation logic using BODS features. Implement data quality rules for validation, cleansing, and standardization, alongside profiling and validation pipelines. Develop AI-powered data engineering agents to analyze metadata, recommend transformations, and propose data quality rules. Implement retrieval-grounded assistance using metadata catalogs and business rules.

Establish automated validation strategies and evaluation harnesses for AI behaviors, ensuring measurable quality thresholds for releases. Optimize ETL jobs for performance and scalability, implementing error handling and recovery mechanisms for reliable operations. Monitor pipelines, perform root cause analysis for failures, and leverage AI diagnostics for issue remediation. Support modernization initiatives by integrating BODS pipelines with cloud platforms and collaborating with cross-functional teams to deliver end-to-end data solutions.

A strong foundation in SAP BusinessObjects Data Services (BODS) ETL development and operations is essential, complemented by a solid understanding of data integration patterns, transformation logic, and enterprise data quality practices. Experience in designing reliable, scalable data pipelines with a focus on performance and governance is required. Proficiency in AI-native capabilities, including tool-augmented workflows, retrieval-grounded recommendations, and evaluation loops, is a key requirement.

Beneficial skills include knowledge of data warehousing, metadata management, lineage concepts, and data governance frameworks. Experience integrating ETL platforms with cloud data ecosystems and modern analytics tools is advantageous. Scripting or automation skills to support pipeline orchestration and operational tooling will further strengthen your profile. A minimum of 3 years of experience is expected for this role, with a full-time education of 15 years required.