Data Architect – AI Model Training | Remote

Data Architect AI Model Training Work SnapshotJob Type: ContractLocation: RemoteCompensation: Up to $140 per hourLevel: Middle to Senior Level Roles & ResponsibilitiesEvaluate AI-generated data architecture content for technical accuracy, scalability, governance alignment, and architectural reasoning qualityReview AI-generated analyses, explanations, recommendations, and design decisions related to enterprise data platforms, cloud data warehouses, lakehouse architectures, metadata systems, and large-scale analytical environmentsChallenge advanced AI systems with realistic Data Architect prompts involving enterprise integration patterns, data governance frameworks, cloud architecture decisions, semantic modeling, and analytics ecosystemsAnalyze AI-generated solutions involving conceptual, logical, and physical data modeling, normalization, dimensional modeling, schema design, semantic layers, and enterprise data domain modelingIdentify architectural flaws, scalability risks, incorrect assumptions, governance gaps, missing tradeoffs, security concerns, and weak reasoning in AI-generated data architecture outputsReview and refine AI-generated prompts, responses, technical recommendations, architectural explanations, and implementation guidance to ensure alignment with modern enterprise data architecture best practicesEvaluate whether AI outputs appropriately account for scalability, performance, maintainability, observability, data quality, lineage, governance, security, privacy, and regulatory compliance requirementsAssess AI-generated reasoning related to cloud data platforms, orchestration patterns, ETL/ELT pipelines, master data management, metadata systems, and enterprise analytics strategiesInterpret and assess architecture-related artifacts including data models, integration workflows, platform blueprints, governance standards, architecture roadmaps, and technical implementation plansCompare and rank multiple AI-generated architecture responses based on technical correctness, completeness, clarity, scalability awareness, governance alignment, and usefulness to enterprise stakeholdersProvide structured feedback documenting reasoning gaps, unsupported assumptions, architectural risks, incomplete tradeoff analysis, weak governance considerations, and unclear technical communicationSupport benchmarking initiatives by designing, reviewing, validating, and calibrating enterprise data architecture tasks across varying levels of complexity and organizational scaleHelp improve AI communication standards for data architecture topics by ensuring outputs demonstrate systems thinking, practical engineering judgment, architectural clarity, and enterprise readinessEnsure AI-generated data architecture content reflects sound enterprise information management practices, durable platform design principles, and realistic implementation considerationsSupport AI model improvement through annotation workflows, architecture evaluations, technical QA reviews, response ranking, and structured enterprise data documentation processes RequirementsEducation: Bachelor s degree in Computer Science, Information Systems, Data Engineering, Software Engineering, Mathematics, or a related technical field required; advanced degree or relevant architecture certifications preferredMinimum 4+ years of professional experience in data architecture, enterprise data strategy, data engineering leadership, or closely related technical architecture rolesStrong hands-on experience with enterprise data platforms, cloud data warehouses, lakehouse architectures, data pipelines, metadata systems, and large-scale analytical ecosystemsDeep understanding of conceptual, logical, and physical data modeling including normalization, dimensional modeling, schema design, semantic layers, and enterprise data domain modelingStrong knowledge of data governance, data quality management, data lineage, privacy controls, security frameworks, master data management, and regulatory compliance considerationsProven experience designing scalable data architectures across cloud platforms such as AWS, Azure, or Google Cloud, including modern warehouse, lakehouse, and orchestration patternsDemonstrated ability to translate business requirements into durable data architecture decisions, technical standards, platform strategies, implementation roadmaps, and governance frameworksExperience evaluating scalability, performance optimization, reliability, maintainability, integration complexity, and enterprise data platform tradeoffs strongly preferredExcellent analytical thinking and attention to detail when evaluating architecture decisions, governance models, platform constraints, and technical feasibilityStrong written communication skills with the ability to explain complex data architecture concepts clearly and concisely for engineers, analysts, executives, and cross-functional stakeholdersAbility to evaluate AI-generated technical content for architectural correctness, governance maturity, scalability awareness, implementation realism, and enterprise applicabilityPrevious experience with AI data training, architecture annotation, technical QA, or evaluation of AI-generated technical content strongly preferredFamiliarity with AI systems and tools such as ChatGPT, Gemini, Claude, Perplexity, or similar platforms preferredReliable remote work practices, confidentiality handling, and consistency across structured data architecture review workflows required