Data Engineer for AI Coding Agents (Remote)

mercor; United States; Remote; USD 94,000 - 127,000; Full time

Job summary

Mercor, a San Francisco-based company, is hiring a Data Engineer (Coding Agent Experience) for a remote contract. You will use frontier AI coding agents to tackle complex data engineering tasks and review model-generated implementations across ETL pipelines, data warehouses, analytics platforms, and distributed data systems.

You will identify bugs and edge cases, compare outputs from multiple frontier models, and apply engineering judgment to realistic data scenarios while operating large-scale

Qualifications

• 2+ years of professional data engineering experience.
• Experience building ETL pipelines, data warehouses, analytics platforms, or distributed data systems.
• Regular use of AI coding agents such as Cursor, Claude Code, Codex, Windsurf, Gemini CLI, or similar tools.
• Ability to evaluate model-generated data infrastructure and pipeline implementations.

Responsibilities

• Use frontier AI coding agents to complete and evaluate complex data engineering tasks.
• Review model-generated implementations involving ETL pipelines, data warehouses, analytics platforms, and distributed data systems.
• Identify bugs, edge cases, scalability issues, and failure modes in model outputs.
• Compare outputs from multiple frontier models and assess their strengths and weaknesses.
• Apply professional engineering judgment to realistic data engineering scenarios.

Skills

Data engineering

ETL pipelines

Analytics platforms

Tools

Cursor

Claude Code

Codex

Windsurf

Gemini CLI

Job description

Mercor, a San Francisco-based company, is hiring a Data Engineer (Coding Agent Experience) for a remote contract. You will use frontier AI coding agents to tackle complex data engineering tasks and review model-generated implementations across ETL pipelines, data warehouses, analytics platforms, and distributed data systems.

You will identify bugs and edge cases, compare outputs from multiple frontier models, and apply engineering judgment to realistic data scenarios while operating large-scale