Senior Data Architect for AI Model Evaluation (Remote)

Job Type: Contractor

Location: Remote

Role Description

If you’re a senior Data Architect who thrives on precision, systems thinking, governance, and scalable data design, this is a unique opportunity to contribute directly to how the next generation of AI systems reason about complex enterprise data environments. We’re looking for experienced data architecture professionals; in this role, you’ll challenge and evaluate advanced language models on Data Architect topics. Your work will help strengthen how AI systems analyze technical requirements, reason through architecture decisions, and communicate accurate guidance to technical and business stakeholders.

Your Profile

• 4+ years of professional experience in data architecture, enterprise data strategy, data engineering leadership, or a closely related technical role.
• Significant hands‑on work with enterprise data platforms, cloud data warehouses, lakehouse architectures, data pipelines, metadata systems, and/or large‑scale analytical environments.
• Deep knowledge of conceptual, logical, and physical data modeling, including normalization, dimensional modeling, schema design, semantic layers, and data domain modeling.
• Strong understanding of data governance, data quality, data lineage, privacy, security, master data management, and regulatory considerations in enterprise data environments.
• Proven experience designing scalable data architectures across cloud platforms such as AWS, Azure, or Google Cloud, including modern data warehouses, data lakes, and orchestration patterns.
• Demonstrated experience in translating business requirements into durable data architecture decisions, technical standards, platform recommendations, and implementation roadmaps.
• Bachelor’s degree in computer science, information systems, data engineering, software engineering, mathematics, or a related field; advanced degree or relevant architecture certifications are preferred.

Key Responsibilities

• Evaluate AI-generated responses for accuracy, completeness, clarity, and sound architectural reasoning across data architecture topics.
• Challenge advanced language models with realistic Data Architect scenarios involving enterprise data platforms, governance models, integration patterns, and analytics ecosystems.
• Review and refine AI-generated prompts, answers, explanations, and technical recommendations to ensure they reflect sound data architecture principles.
• Provide structured feedback on model outputs, identifying gaps in reasoning, incorrect assumptions, architectural risks, or missing tradeoffs.
• Assess whether AI responses appropriately address scalability, performance, security, compliance, data quality, and maintainability.
• Shape AI communication standards for technical explanations, ensuring responses are clear, practical, and appropriate for data engineers, analysts, executives, and cross‑functional stakeholders.
• Support benchmarking efforts by helping define what strong, weak, and misleading AI-generated data architecture responses look like.
• Create or review domain-specific evaluation tasks that test model understanding of data modeling, cloud architecture, data governance, and enterprise information management.