SAP BTP Datasphere-1907 at RNR Global… · Hyderabad

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).

2) 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.

3) 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.

4) 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 modelswhile maintaining clear rules for what s exposed for consumption.
• Embed quality controls, validation checks, and reproducible transformations as part of the delivery lifecycle.

5) Agentic Orchestration & Tooling

• Build data agents that can plan, call tools (query/metadata/lineage), retrieve context, and generate answers with citations—backed by deterministic checks and fallback behaviors.
• Implement prompt templates, tool schemas, and safe action boundaries for enterprise-grade usage.

6) Evaluation, Observability & Responsible AI

• Establish offline/online evaluation loops (golden questions, regression suites, behavior tests) for conversational analytics and data agents.
• Add telemetry for AI interactions (latency, grounding rate, failure modes) to improve reliability and cost efficiency.

7) Integration & Collaboration

• Partner closely with business, data governance, and platform teams to align data products with real decisions and operational workflows.
• Drive reusable patterns and accelerators for repeatable delivery across domains.