Remote Data Engineer & AI Code Reviewer

AuraOne; United States; On-site; USD 68,880 - 165,312; Part time

Job summary

AuraOne is seeking a Data Engineer on a remote, contractor track to evaluate production code, debug traces, and AI outputs against correctness standards. You will reproduce failures, write minimal failing tests, and explain fixes to help the modeling team target gaps.

You will review code in sandboxed environments, grade data engineering solutions for correctness and edge cases, and document recurring failures to improve future reviews.

Qualifications

• Strong day-job engineering experience — you can read, run, and debug unfamiliar code for Data Engineer work.
• Comfort writing concise unit tests that capture a single failure mode.
• Familiarity with at least one of: pytest, Jest, JUnit, Go test, RSpec, or equivalent.
• Clear written reasoning — your review note has to convince another senior engineer.
• Reliable async availability for at least 10 hours per week.

Responsibilities

• Run and reproduce candidate code outputs in a sandboxed environment for Data Engineer assignments.
• Grade data engineering solutions for correctness, complexity, idiomatic style, and edge-case handling.
• Write minimal failing tests that demonstrate the bug a model output missed.
• Compare paired solutions and rank them with a written rationale tied to the rubric.
• Tag failure modes (compile error, runtime crash, off-by-one, security issue) with severity scores.
• Document recurring code-generation failures so the modeling team can target them.
• Calibrate against gold-standard reviews to keep inter-rater agreement above target.

Skills

Code review

Debugging

Unit testing

Software engineering judgment

Data engineering

AI

JAVA

JavaScript

MySQL

PostgreSQL

Python

Tools

pytest

Jest

JUnit

Go test

RSpec

Job description

AuraOne is seeking a Data Engineer on a remote, contractor track to evaluate production code, debug traces, and AI outputs against correctness standards. You will reproduce failures, write minimal failing tests, and explain fixes to help the modeling team target gaps.

You will review code in sandboxed environments, grade data engineering solutions for correctness and edge cases, and document recurring failures to improve future reviews.