We are sharing a specialised part-time consulting opportunity for experienced data engineers with hands-on expertise in ETL pipelines, data warehouses, analytics platforms, distributed data systems, and AI-assisted coding workflows.
This sprint-based role supports an advanced AI research initiative focused on evaluating frontier coding models through realistic data engineering tasks. Selected professionals will use AI coding agents to complete technical assignments, review model-generated data infrastructure, identify scalability and reliability issues, and compare model performance across practical engineering scenarios.
Key Responsibilities
Data Engineering Evaluation
- Complete and evaluate complex data engineering tasks using frontier AI coding agents
- Review implementations involving ETL and ELT pipelines, data warehouses, analytics platforms, and distributed systems
- Assess technical correctness, maintainability, scalability, and production readiness
- Apply professional engineering judgment to realistic data infrastructure scenarios
AI Coding Agent Testing
- Use AI coding agents within practical data engineering workflows
- Evaluate how effectively models interpret requirements and implement technical solutions
- Identify bugs, edge cases, incomplete implementations, and failure modes
- Assess where generated solutions require correction or additional engineering work
Pipeline & Infrastructure Review
- Review model-generated data pipelines and supporting infrastructure
- Evaluate ingestion, transformation, storage, orchestration, and data-processing logic
- Identify scalability, reliability, performance, and data-quality concerns
- Assess implementations intended for large-scale or distributed environments
Model Comparison & Technical Feedback
- Compare solutions produced by multiple frontier coding models
- Assess differences in architecture, implementation quality, technical reasoning, and reliability
- Identify recurring strengths and weaknesses across model outputs
- Provide clear written assessments explaining relevant engineering trade-offs
Ideal Profile
Strong candidates may have:
- At least 2 years of professional data engineering experience
- Hands-on experience building ETL pipelines, data warehouses, analytics platforms, or distributed data systems
- Experience operating or supporting large-scale data platforms
- Regular use of AI coding agents within engineering workflows
- Strong ability to evaluate model-generated data infrastructure and pipeline implementations
- Experience debugging complex data-processing or infrastructure issues
- Strong technical judgment, written communication, and attention to detail
- Ability to work effectively within short, intensive project sprints
Educational Background
- A degree in computer science, data engineering, software engineering, information systems, or a related technical discipline may be helpful
- Advanced technical training in distributed systems, databases, cloud infrastructure, or data platforms may strengthen an application
- Equivalent professional experience building production data systems may also be considered
- Practical engineering depth is particularly valuable for this engagement
Nice to Have
- Experience with large-scale distributed data platforms
- Familiarity with cloud-based data warehouses and analytics systems
- Knowledge of workflow orchestration, data transformation, and pipeline monitoring
- Experience with Cursor, Claude Code, Codex, Windsurf, Gemini CLI, or comparable AI coding tools
- Background in data quality, performance optimisation, or infrastructure reliability
- Experience reviewing code or technical implementations created by other engineers
- Previous exposure to AI evaluation, benchmark development, or structured technical review
Why This Opportunity
- Work directly with frontier AI coding agents on realistic data engineering problems
- Apply professional data engineering expertise to advanced AI evaluation
- Review complex pipelines, data infrastructure, and distributed-system implementations
- Identify subtle technical and scalability failures in model-generated solutions
- Compare multiple coding systems across practical engineering workflows
- Participate in intensive technical sprints with task-based compensation
Contract Details
- Independent contractor role
- Fully remote with flexible scheduling
- Sprint-based project with task windows typically spanning approximately 12–24 hours
- Compensation is $400 per accepted task
- Typical tasks require approximately 2–3 hours after ramp-up
- Compensation is tied to successfully accepted work
- Competitive compensation equivalent to up to approximately $75 per hour depending on accepted work and task duration
- Weekly payments via Stripe or Wise
- Work may include pipeline implementation review, AI coding-agent evaluation, debugging, scalability analysis, and model comparison
- Projects may be extended, shortened, or adjusted depending on scope and performance
- Work will not involve access to confidential or proprietary information from any employer, client, or institution
About the Platform
This opportunity is available through 24-MAG LLC. We connect experienced professionals with remote consulting opportunities across technical, evaluation, and project-based workstreams.
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