Quality Engineer
Project Role Description : Enables full stack solutions through multi-disciplinary team planning and ecosystem integration to accelerate delivery and drive quality across the application lifecycle. Performs continuous testing for security, API, and regression suite.
Creates automation strategy, automated scripts and supports data and environment configuration. Participates in code reviews, monitors, and reports defects to support continuous improvement activities for the end-to-end testing process.
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
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- AI Native Data Product Engineering (on Datasphere)
Build semantic models that are fit for both analytics and AI consumption (clear entity definitions, measures, hierarchies, lineage-friendly design).
- Retrieval + Grounding (RAG) over Enterprise 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)
- Lakehouse style Layering & Data Quality by Design
Embed quality controls, validation checks, and reproducible transformations as part of the delivery lifecycle.
- Agentic Orchestration & Tooling
Implement prompt templates, tool schemas, and safe action boundaries for enterprise-grade usage.
- Evaluation, Observability & Responsible AI
Add telemetry for AI interactions (latency, grounding rate, failure modes) to improve reliability and cost efficiency.
- Integration & Collaboration
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.
Secondary / Strongly Beneficial SkillsMigration/modernization experience leveraging BW bridge style transition patterns.
Layered architecture implementation (Bronze/Silver/Gold) for scalable analytics delivery.
Familiarity with vector search / embedding pipelines (when integrating external AI retrieval components).
What This Role Does Not Center On
Training foundation models from scratch (the emphasis is on building agentic apps and governed retrieval on enterprise data).AI assisted only delivery this role owns the AI behavior (grounding, evaluation, safety) end to end.
Value DeliveredFaster path from data to decision through conversational + agentic analytics grounded in governed Datasphere models.
Scalable modernization of hybrid data estates via patterns like BW bridge.
Higher trust AI outputs by implementing layered quality + evaluation loops.
Additional InformationA 15 years full time education is required