AI-Driven Data Engineer (Remote Contract)

mercor; United States; Remote; USD 110,000 - 138,000; Full time

Job summary

Mercor, a San Francisco-based AI talent network, seeks a Data Engineer (Coding Agent Experience) for a remote contract role. You will use frontier AI coding agents to complete and evaluate complex data engineering tasks, spanning ETL pipelines, data warehouses, analytics platforms, and distributed data systems.

You will review model-generated implementations, identify bugs and scalability issues, compare outputs across models, and apply engineering judgment to realistic data infrastructure

Qualifications

• 2+ years 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 data engineering tasks.
• Review model-generated 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 strengths and weaknesses.
• Apply professional engineering judgment to realistic data engineering scenarios.

Skills

Data engineering

ETL pipelines

Data warehouses

Analytics platforms

Distributed data systems

AI coding agents usage

Model evaluation

Tools

Cursor

Claude Code

Codex

Windsurf

Gemini CLI

Job description

Mercor, a San Francisco-based AI talent network, seeks a Data Engineer (Coding Agent Experience) for a remote contract role. You will use frontier AI coding agents to complete and evaluate complex data engineering tasks, spanning ETL pipelines, data warehouses, analytics platforms, and distributed data systems.

You will review model-generated implementations, identify bugs and scalability issues, compare outputs across models, and apply engineering judgment to realistic data infrastructure