Data Architect – AI Model Training | Remote

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Crossing Hurdles
United States, US
  • IT

Data Architect AI Model Training Work Snapshot

  • Job Type: Contract
  • Location: Remote
  • Compensation: Up to $140 per hour
  • Level: Middle to Senior Level

Roles & Responsibilities

  • Evaluate AI-generated data architecture content for technical accuracy, scalability, governance alignment, and architectural reasoning quality
  • Review AI-generated analyses, explanations, recommendations, and design decisions related to enterprise data platforms, cloud data warehouses, lakehouse architectures, metadata systems, and large-scale analytical environments
  • Challenge advanced AI systems with realistic Data Architect prompts involving enterprise integration patterns, data governance frameworks, cloud architecture decisions, semantic modeling, and analytics ecosystems
  • Analyze AI-generated solutions involving conceptual, logical, and physical data modeling, normalization, dimensional modeling, schema design, semantic layers, and enterprise data domain modeling
  • Identify architectural flaws, scalability risks, incorrect assumptions, governance gaps, missing tradeoffs, security concerns, and weak reasoning in AI-generated data architecture outputs
  • Review and refine AI-generated prompts, responses, technical recommendations, architectural explanations, and implementation guidance to ensure alignment with modern enterprise data architecture best practices
  • Evaluate whether AI outputs appropriately account for scalability, performance, maintainability, observability, data quality, lineage, governance, security, privacy, and regulatory compliance requirements
  • Assess AI-generated reasoning related to cloud data platforms, orchestration patterns, ETL/ELT pipelines, master data management, metadata systems, and enterprise analytics strategies
  • Interpret and assess architecture-related artifacts including data models, integration workflows, platform blueprints, governance standards, architecture roadmaps, and technical implementation plans
  • Compare and rank multiple AI-generated architecture responses based on technical correctness, completeness, clarity, scalability awareness, governance alignment, and usefulness to enterprise stakeholders
  • Provide structured feedback documenting reasoning gaps, unsupported assumptions, architectural risks, incomplete tradeoff analysis, weak governance considerations, and unclear technical communication
  • Support benchmarking initiatives by designing, reviewing, validating, and calibrating enterprise data architecture tasks across varying levels of complexity and organizational scale
  • Help improve AI communication standards for data architecture topics by ensuring outputs demonstrate systems thinking, practical engineering judgment, architectural clarity, and enterprise readiness
  • Ensure AI-generated data architecture content reflects sound enterprise information management practices, durable platform design principles, and realistic implementation considerations
  • Support AI model improvement through annotation workflows, architecture evaluations, technical QA reviews, response ranking, and structured enterprise data documentation processes

Requirements

  • Education: 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 preferred
  • Minimum 4 years of professional experience in data architecture, enterprise data strategy, data engineering leadership, or closely related technical architecture roles
  • Strong hands-on experience with enterprise data platforms, cloud data warehouses, lakehouse architectures, data pipelines, metadata systems, and large-scale analytical ecosystems
  • Deep understanding of conceptual, logical, and physical data modeling including normalization, dimensional modeling, schema design, semantic layers, and enterprise data domain modeling
  • Strong knowledge of data governance, data quality management, data lineage, privacy controls, security frameworks, master data management, and regulatory compliance considerations
  • Proven experience designing scalable data architectures across cloud platforms such as AWS, Azure, or Google Cloud, including modern warehouse, lakehouse, and orchestration patterns
  • Demonstrated ability to translate business requirements into durable data architecture decisions, technical standards, platform strategies, implementation roadmaps, and governance frameworks
  • Experience evaluating scalability, performance optimization, reliability, maintainability, integration complexity, and enterprise data platform tradeoffs strongly preferred
  • Excellent analytical thinking and attention to detail when evaluating architecture decisions, governance models, platform constraints, and technical feasibility
  • Strong written communication skills with the ability to explain complex data architecture concepts clearly and concisely for engineers, analysts, executives, and cross-functional stakeholders
  • Ability to evaluate AI-generated technical content for architectural correctness, governance maturity, scalability awareness, implementation realism, and enterprise applicability
  • Previous experience with AI data training, architecture annotation, technical QA, or evaluation of AI-generated technical content strongly preferred
  • Familiarity with AI systems and tools such as ChatGPT, Gemini, Claude, Perplexity, or similar platforms preferred
  • Reliable remote work practices, confidentiality handling, and consistency across structured data architecture review workflows required
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