As a Custom Software Engineer, you will be instrumental in developing cutting-edge custom software solutions. This role involves designing, coding, and enhancing components across various systems and applications.

You will leverage modern frameworks and agile practices to deliver scalable, high-performing solutions meticulously tailored to specific business needs.

This position focuses on building AI-native, data-centric products on SAP BTP Datasphere, merging strong enterprise data warehousing and semantic modeling expertise with agentic AI architectures. The goal is to transcend traditional dashboards and create intelligent data experiences, including data agents, conversational analytics, and grounded insights. These will be built upon governed Datasphere models and integrated enterprise sources, positioning SAP Datasphere as a robust data warehousing solution with advanced integration capabilities.

Design and implement governed data products within Datasphere, utilizing concepts like Spaces and shareable models/views to facilitate data exploration, transformation, and sharing across domains.

Construct semantic models optimized for both analytics and AI consumption, ensuring clear entity definitions, measures, hierarchies, and a lineage-friendly design.

Create grounded AI experiences by connecting LLM applications to curated Datasphere models and enterprise sources, guaranteeing traceable and governed data responses.

Engineer retrieval strategies that adhere to domain boundaries, freshness requirements, and access controls for reliable and compliant AI outputs.

Enable transition paths from legacy warehouse investments by applying approaches like reusing SAP BW models and skills with Datasphere / BW bridge, supporting phased cloud modernization.

Implement layered design patterns (e.g., Bronze/Silver/Gold) for data landing, cleansing, and publishing analytics-ready models, while enforcing clear consumption rules.

Embed quality controls, validation checks, and reproducible transformations throughout the delivery lifecycle.

Build data agents capable of planning, tool execution (query/metadata/lineage), context retrieval, and answer generation with citations, supported by deterministic checks and fallback mechanisms.

Establish offline/online evaluation loops for conversational analytics and data agents, incorporating telemetry for AI interactions to enhance reliability and cost efficiency.

Partner with business, data governance, and platform teams to align data products with operational workflows and drive reusable patterns for consistent delivery across domains.

A strong foundation in SAP BTP Datasphere, encompassing data modeling, spaces, sharing patterns, and enterprise semantic design.

Demonstrated expertise in data warehousing fundamentals and the ability to translate business domains into governed analytical models.

Hands-on experience in building with LLMs and RAG, including retrieval, grounding, prompt and tool design, and evaluation techniques.

Solid software engineering fundamentals, including a strong understanding of testability, CI/CD principles, and reliable integration methods.

Familiarity with migration and modernization efforts, particularly leveraging BW bridge style transition patterns, is highly beneficial.

Experience in implementing layered architectures (Bronze/Silver/Gold) for scalable analytics delivery is advantageous.

Proficiency with vector search and embedding pipelines for integrating external AI retrieval components is a plus.