[Remote] Remote | Data Infrastructure Evaluation Engineer — Up to $90/hour

Note: The job is a remote job and is open to candidates in USA. 24-MAG LLC is offering a specialized remote consulting opportunity for experienced data engineers. The role focuses on evaluating data engineering implementations, reviewing technical workflows, and assessing data infrastructure using modern coding agents.

Responsibilities

• Use modern coding agents to complete and evaluate complex data engineering tasks
• Review generated implementations involving ETL pipelines, data warehouses, analytics platforms, and distributed data systems
• Assess technical outputs for correctness, scalability, maintainability, reliability, and production-readiness
• Apply professional data engineering judgment to realistic infrastructure and pipeline scenarios
• Evaluate pipeline architecture, data transformation logic, ingestion workflows, orchestration patterns, and data quality checks
• Review data warehouse and analytics platform implementations for performance, accuracy, structure, and maintainability
• Identify bugs, edge cases, scalability issues, failure modes, and weak assumptions in data engineering outputs
• Provide structured feedback on data flow, system design, reliability, and implementation quality
• Compare outputs from multiple coding agents and assess their strengths, weaknesses, accuracy, and practical usefulness
• Identify where generated solutions succeed, where they fail, and where additional engineering judgment is required
• Evaluate whether generated data infrastructure reflects real-world data engineering standards
• Document technical review findings clearly for project teams and quality evaluation workflows
• Produce clear, structured evaluations of data engineering tasks and generated outputs
• Explain reasoning around pipeline design, data modelling, warehouse architecture, distributed systems, scalability, and failure handling
• Support technical assessment workflows by documenting accepted work, improvement areas, and practical engineering conclusions
• Help ensure outputs reflect production-scale data engineering expectations

Skills

• 2+ years of professional data engineering experience
• Hands-on experience building ETL pipelines, data warehouses, analytics platforms, distributed data systems, or large-scale data infrastructure
• Regular use of AI coding agents such as Cursor, Claude Code, Codex, Windsurf, Gemini CLI, or comparable tools
• Ability to evaluate generated data infrastructure and pipeline implementations for correctness, scalability, and reliability
• Strong understanding of data modelling, data quality, orchestration, distributed processing, warehouse design, and pipeline maintainability
• Clear written communication skills and comfort documenting technical reasoning in a remote, project-based environment
• A degree in Computer Science, Data Engineering, Software Engineering, Computer Engineering, Information Systems, Statistics, or a related technical field is helpful
• Equivalent professional experience in data engineering, analytics engineering, distributed systems, or production data platforms is also highly relevant
• Experience supporting large-scale data platforms is strongly preferred
• Experience with Python, SQL, Spark, Airflow, dbt, Kafka, Flink, Snowflake, BigQuery, Redshift, Databricks, or comparable data tools
• Familiarity with cloud data platforms, data lakehouse architecture, orchestration systems, batch processing, streaming pipelines, or data quality frameworks
• Experience with CI/CD workflows, Docker, Kubernetes, Terraform, observability tooling, or infrastructure automation in data environments
• Background in technical code review, data architecture review, pipeline performance evaluation, or large-scale analytics systems
• Strong comfort working in sprint-based project environments with focused technical assessment windows

Benefits

• Remote consulting work aligned with data engineering, coding agent, and technical evaluation expertise
• Fully remote and flexible scheduling
• Sprint-based, project-based availability
• Payments are made weekly via Stripe or Wise based on services rendered
• Projects may be extended, shortened, adjusted, or concluded based on project needs and performance

Company Overview