Custom Software Engineer

Project Role : Custom Software Engineer Project Role Description : Develop custom software solutions to design, code, and enhance components across systems or applications. Use modern frameworks and agile practices to deliver scalable, high-performing solutions tailored to specific business needs. Must have skills : SAP BTP Datasphere Good to have skills : NA Minimum 3 year(s) of experience is required Educational Qualification : 15 years full time education

Summary

AI Powered Tech Talent Build AI native, data centric products on SAP BTP Datasphere by combining strong enterprise data warehousing and semantic modeling expertise with agentic AI architectures (LLMs + tools + retrieval + evaluation). The focus is to move beyond dashboards into intelligent data experiences—data agents, conversational analytics, and grounded insights—built on governed Datasphere models and integrated enterprise sources. SAP Datasphere is positioned as a data warehousing solution with integration capabilities. Core Responsibilities

1. AI Native Data Product Engineering (on Datasphere)

Design and implement governed data products using Datasphere concepts such as Spaces and shareable models/views, enabling teams to explore, transform, and share curated datasets across domains. Build semantic models that are fit for both analytics and AI consumption (clear entity definitions, measures, hierarchies, lineage-friendly design).

1. Retrieval + Grounding (RAG) over Enterprise Data

Create grounded AI experiences by connecting LLM applications to Datasphere s curated models and enterprise sources (SAP and non SAP), ensuring responses are traceable to governed data. Engineer retrieval strategies that respect domain boundaries (spaces), freshness needs, and access controls, so AI outputs remain reliable and compliant.

1. Hybrid Modernization & Migration (BW bridge patterns)

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

1. Lakehouse style Layering & Data Quality by Design

Implement layered design patterns (e.g., Bronze/Silver/Gold) to land raw data, cleanse/validate, and publish analytics ready models—while maintaining clear rules for what s exposed for consumption. Embed quality controls, validation checks, and reproducible transformations as part of the delivery lifecycle.