This role focuses on engineering AI-native, data-centric products leveraging SAP BTP Datasphere. You will combine expertise in enterprise data warehousing and semantic modeling with agentic AI architectures, including LLMs and tools.

The goal is to move beyond traditional dashboards and create intelligent data experiences like data agents and conversational analytics. These will be built upon governed Datasphere models and integrated enterprise sources, positioning SAP Datasphere as a robust data warehousing and integration solution.

Design and implement governed data products within Datasphere, utilizing concepts like Spaces and shareable models for data exploration and transformation. Build semantic models suitable for both analytics and AI consumption, ensuring clear entity definitions and lineage. Develop grounded AI experiences by connecting LLM applications to curated Datasphere models and enterprise data sources for traceable responses.

Engineer retrieval strategies that adhere to domain boundaries and access controls for reliable AI outputs. Facilitate transitions from legacy warehouse investments using approaches like BW bridge patterns for phased cloud modernization. Implement layered design patterns (e.g., Bronze/Silver/Gold) for data landing, cleansing, and analytics-ready model publishing, embedding quality controls throughout the delivery lifecycle.

A minimum of 2-5 years of experience is required, with a strong foundation in SAP BTP Datasphere, including data modeling, spaces, sharing patterns, and enterprise semantic design. Proven ability in data warehousing fundamentals and translating business domains into governed analytical models is essential.

Hands-on experience building with LLMs and RAG, covering retrieval, grounding, prompt/tool design, and evaluation, is a must. Solid software engineering fundamentals, including testability, a CI/CD mindset, and reliable integrations, are also critical. Migration/modernization experience with BW bridge patterns and layered architecture implementation are considered strongly beneficial.