Quality Engineer

Project Role : 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 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). 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 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. 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.