
Data Entry Keyer - Remote Contract
Listing checked September 17, 2026 · pay as published by Micro1
Overview
micro1 seeks Data Entry Keyers to support an advanced AI benchmark project for a customer. You will create evaluation tasks that test how AI agents handle messy data entry and validation scenarios. The work involves building datasets with CSVs, PDFs, spreadsheets, and technical documents that contain realistic errors like malformed records, missing fields, and inconsistent formats. No AI background is needed. Your data entry expertise and attention to detail are what count.
What You'll Do6
- 1Create expert-level evaluation tasks that mimic real-world data entry and validation problems for AI agents.
- 2Build and organize large, complex datasets using CSVs, PDFs, spreadsheets, and technical documents with intentional errors such as malformed records, missing data, and format mismatches.
- 3Specify the correct end state for each task, including exact error cases and reconciliation steps an AI must perform.
- 4Write detailed grading rubrics with 35 or more criteria to assess AI agent outputs on accuracy and completeness in error detection.
- 5Make sure every task reflects the complexity and standards found in regulated or audit-sensitive settings.
- 6Work with others through written and verbal communication to refine evaluation materials asynchronously.
Requirements7
- 1Deep experience in data entry, quality assurance, or data validation within regulated or audit-sensitive fields like healthcare claims, finance back office, or legal operations.
- 2Proven track record of meeting and documenting high accuracy standards, with measurable error rates and results.
- 3Strong ability to spot and analyze data inconsistencies, silent truncations, and formatting irregularities across different file types.
- 4Proficient written English for drafting complex evaluation tasks and thorough grading rubrics.
- 5Exceptional attention to detail and a disciplined, process-driven approach to work.
- 6Experience on projects where compliance, accuracy, or data integrity carried high stakes.
- 7Comfort working alone and communicating proactively in remote, distributed teams.
Who Should Apply
This role fits someone with a background in data entry, QA, or validation who has worked in regulated or audit-sensitive environments such as healthcare claims, finance back office, or legal operations. You should be able to show measurable accuracy results and feel at ease building complex datasets with deliberate errors. The work is less suitable for people who need constant supervision or who dislike writing detailed rubrics. Candidates often get rejected when they cannot provide concrete examples of error rates or when their written English lacks the precision needed for grading criteria. Another common reason for low fit scores is not having experience with silent truncations or format reconciliation across multiple file types.
Salary Insight
Compensation is $20 to $35 per hour, but pay is output-based. You are paid per task that meets project specifications, and the time to complete work varies by experience and workflow. Minimum submission requirements apply, including a minimum number of tasks per week.
Pay and demand for Data Engineering roles
AggregatedTypical pay
$80/hour
This role
$20–$35/hr
Most Data Engineering roles pay $50–$95 per hour. This role's pay sits below that range.
Based on 132 similar roles that publish pay · 23 publish only a top rate; those count at the rate they gave
Rates shown per hour. Yearly and monthly pay converted; one-time fees and non-USD pay are not included.
- Live similar roles
- 156
- Listed in last 30 days
- 61
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- 92%
Hiring most right now: micro1 (85) · SME Careers (13) · Handshake AI (12)
Most requested skills · share of roles
- Python38%
- sql15%
- AWS10%
- data analysis8%
Figures from Data Engineering roles live on NearSkill when this page loaded. A role can close before you apply, so check the listing itself.
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Compensation
$20–35/hr
Required Skills
Application Tip
In your application, include specific examples of error rates you have achieved and the file types you have worked with, such as CSVs, PDFs, or spreadsheets. Mention any experience with silent truncations or format reconciliation, and quantify your accuracy results to show you meet the high standards for this project.
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