A Data Scientist utilizes skills and experience to systematically answer questions using data to provide actionable recommendations. Commonly utilizes advanced statistical analysis and machine learning techniques. Common responsibilities also include data cleaning and data management.
Lead architecture and development of enterprise data products using Denodvirtualization and semantic modeling. Define and govern canonical business models, ontologies, business glossaries, and semantic layers. Establish architecture standards, naming conventions, governance frameworks, and development best practices. Provide technical leadership tData Engineers, AI Engineers, LLMOps Engineers, and platform teams.
Denod& Data Product:
Design and implement Virtual Data Products Develop complex joins, unions, aggregations, and business transformations across multiple source systems. Define reusable semantic-layer patterns that support reporting, analytics, APIs, and AI agents. Optimize Denodperformance through caching, query pushdown, aggregation strategies, and virtualization best practices. Integration & APIs Design API-first data product architectures.
Integrate Denodwith:
AWS services Analytics platforms Data Catalogs AI platforms Enterprise APIs Support semantic consumption for AI agents and business applications. Integration & APIs Design API-first data product architectures.
Integrate Denodwith:
AWS services Analytics platforms Data Catalogs AI platforms Enterprise APIs Support semantic consumption for AI agents and business applications. Required Qualifications 10+ years in Data Engineering, Data Architecture, or Analytics Engineering. 5+ years of Denodimplementation experience. Experience developing enterprise semantic layers and virtualized data products.
Strong knowledge of:
Denodo AWS (Lambda, Glue, ETL, APIs) Data Modeling Ontologies Data Governance API Design Experience with: Generative AI Agentic AI frameworks LLMOps Strong stakeholder engagement and consulting skills. Preferred Qualifications Experience designing AI-ready data platforms. Knowledge of AI Governance standards. Familiarity with Bedrock, OpenAI, Anthropic, Azure OpenAI, or similar platforms. Experience implementing enterprise metadata, lineage, and semantic governance frameworks.
