Join our team as a Custom Software Engineer, focused on building AI-native, data-centric products on SAP BTP Datasphere. This role is key to developing intelligent data experiences like data agents and conversational analytics.

You'll leverage strong enterprise data warehousing and semantic modeling expertise, combined with agentic AI architectures. The objective is to move beyond traditional dashboards and create innovative, governed data solutions.

SAP Datasphere serves as our core data warehousing solution, offering robust integration capabilities for these advanced applications.

Design and implement governed data products on Datasphere, including Spaces and shareable models.

Build semantic models optimized for both analytics and AI consumption, ensuring clear definitions and lineage.

Create grounded AI experiences by connecting LLM applications to Datasphere's curated models and enterprise sources.

Engineer retrieval strategies that respect domain boundaries, freshness, and access controls for reliable AI outputs.

Facilitate cloud modernization by enabling transition paths from legacy warehouse investments using SAP BW bridge patterns.

Implement layered architecture designs (e.g., Bronze/Silver/Gold) for data landing, cleansing, and analytics-ready models.

Embed quality controls and validation checks throughout the delivery lifecycle.

Develop data agents capable of planning, tool execution, context retrieval, and generating cited answers.

Establish evaluation loops for conversational analytics and data agents, ensuring accuracy and reliability.

Implement telemetry for AI interactions to monitor performance and cost efficiency.

Collaborate with business, data governance, and platform teams to align data products with operational workflows.

Drive the adoption of reusable patterns and accelerators for efficient, repeatable delivery.

A minimum of 2 to 5 years of experience is required.

Must possess a strong proficiency in SAP BTP Datasphere, including data modeling, spaces, sharing patterns, and enterprise semantic design.

Demonstrate solid data warehousing fundamentals and the ability to translate business domains into governed analytical models.

Have hands-on experience building with LLMs and RAG, covering retrieval, grounding, prompt/tool design, and evaluation.

Exhibit strong software engineering fundamentals, including testability, a CI/CD mindset, and reliable integration capabilities.

Possess migration/modernization experience leveraging BW bridge style transition patterns.

Familiarity with layered architecture implementation (Bronze/Silver/Gold) for scalable analytics delivery is beneficial.

Experience with vector search/embedding pipelines for integrating external AI retrieval components is a plus.

A full-time education spanning 15 years is a prerequisite for this role.