Data Engineer – AI Model Training | Remote

Crossing Hurdles is seeking a Data Engineer for AI model training. The role involves evaluating AI-generated data engineering content for accuracy and scalability, reviewing technical outputs, and supporting AI model improvements through various engineering tasks.

Responsibilities:

• Evaluate AI-generated data engineering content for technical accuracy, scalability, reliability, and production-readiness
• Review AI-generated analyses, explanations, pipeline designs, SQL queries, orchestration workflows, and implementation recommendations related to modern data engineering systems
• Challenge advanced AI systems with realistic Data Engineer prompts involving SQL optimization, Python workflows, ETL/ELT architecture, orchestration, warehouse/lakehouse design, and production data reliability
• Analyze AI-generated solutions involving data pipelines, distributed systems, batch and streaming workflows, schema design, transformation logic, observability, and analytics-ready datasets
• Identify technical inaccuracies, inefficient implementations, weak assumptions, missing constraints, scalability risks, unreliable workflows, and unsafe recommendations in AI-generated data engineering outputs
• Review and refine AI-generated prompts, responses, reference solutions, evaluation rubrics, and implementation guidance to ensure alignment with senior-level data engineering best practices
• Evaluate whether AI outputs appropriately account for data quality, schema evolution, pipeline reliability, lineage tracking, orchestration dependencies, performance optimization, and operational maintainability
• Assess AI-generated reasoning related to warehouse modeling, transformation strategies, distributed data processing, observability tooling, data contracts, and production debugging workflows
• Interpret and assess data engineering artifacts including SQL transformations, orchestration DAGs, pipeline configurations, warehouse schemas, lineage models, validation checks, and infrastructure workflows
• Compare and rank multiple AI-generated data engineering responses based on correctness, efficiency, clarity, scalability, operational reliability, and usefulness to engineering teams
• Provide structured feedback documenting reasoning gaps, unsupported assumptions, implementation flaws, scalability concerns, missing validations, and unclear technical communication
• Support benchmarking initiatives by designing, reviewing, validating, and calibrating data engineering tasks across varying levels of infrastructure complexity and operational scale
• Help improve AI communication standards for data engineering topics by ensuring outputs demonstrate systems thinking, production awareness, debugging discipline, and practical implementation guidance
• Ensure AI-generated content reflects sound engineering principles for pipeline reliability, warehouse design, orchestration patterns, schema management, and scalable data processing
• Support AI model improvement through annotation workflows, technical QA reviews, response ranking, implementation validation, and structured data engineering documentation processes

Requirements:

• Education: Bachelor s degree in Computer Science, Data Engineering, Information Systems, Statistics, Engineering, or a related technical field required; equivalent professional experience may also be considered
• Minimum 4+ years of professional experience in data engineering with significant hands-on work designing, building, and maintaining production-grade data pipelines
• Deep understanding of SQL, data modeling, ETL/ELT architecture, orchestration frameworks, warehouse/lakehouse patterns, and modern data stack technologies
• Strong experience with platforms and tools such as dbt, Airflow, Snowflake, BigQuery, Databricks, Fivetran, or comparable modern data infrastructure ecosystems
• Strong knowledge of distributed data systems, batch and streaming workflows, schema design, data validation, data observability, lineage management, and pipeline reliability engineering
• Proven experience optimizing complex SQL queries, troubleshooting data quality issues, designing scalable transformations, and supporting analytics or machine learning-ready datasets
• Demonstrated ability to translate ambiguous business or technical requirements into durable data models, reliable pipeline designs, orchestration strategies, and implementation plans
• Excellent analytical thinking and attention to detail when evaluating pipeline correctness, transformation logic, data consistency, and production feasibility
• Strong written communication skills with the ability to explain complex data engineering concepts clearly and concisely for technical and cross-functional audiences
• Ability to evaluate AI-generated technical content for implementation quality, architectural soundness, operational reliability, and engineering realism
• Reliable remote work practices, confidentiality handling, and consistency across structured data engineering review workflows required
• Experience evaluating scalability, performance optimization, fault tolerance, monitoring workflows, orchestration dependencies, and operational debugging strongly preferred
• Previous experience with AI data training, engineering 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