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 (explicit entity definitions, measures, hierarchies, lineage-friendly design).

- 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.

- 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.

- 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.





- 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.

- 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.

- 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.

Primary Skills (AI Native Must Have)

- SAP BTP Datasphere: data modeling, spaces, sharing patterns, enterprise semantic design.

- Strong data warehousing fundamentals and ability to translate business domains into governed analytical models.

- Hands-on building with LLMs + RAG (retrieval, grounding, prompt/tool design, evaluation).

- Solid software engineering fundamentals: testability, CI/CD mindset, reliable integrations.

📌 SAP BTP Datasphere (Bengaluru)
🏢 Fittbot
📍 Bengaluru