SAP BTP Datasphere

Fittbot; Hyderabad, Pune District, Bengaluru; On-site; INR 900,000 - 1,300,000; Full time

Job summary

Fittbot seeks an AI native data product engineer to design governed data products using Datasphere, building analytics- and AI-ready semantic models. You will connect LLM applications to enterprise data, implement retrieval and grounding, and support hybrid modernization patterns.

You'll design layered data architectures with quality controls and create data agents capable of planning, calling tools, and citing sources for enterprise-grade AI.

Qualifications

• Experience designing data products using Datasphere concepts (Spaces, shareable models/views).
• Ability to translate business domains into governed analytical models.
• Hands-on experience with LLMs, retrieval, grounding, and evaluation.

Responsibilities

• Design and implement governed data products using Datasphere concepts.
• Build semantic models fit for analytics and AI consumption with lineage-friendly design.
• Create grounded AI experiences by connecting LLM apps to data sources.
• Engineer retrieval strategies respecting domain boundaries and access controls.
• Enable modernization through BW bridge patterns and cloud-native approaches.
• Implement layered Bronze/Silver/Gold data architectures with quality checks.
• Develop data agents that plan, call tools, retrieve context, and generate cited answers.

Skills

SAP BTP Datasphere

Data warehousing fundamentals

LLMs + RAG

CI/CD

Job description

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.

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.