Data Entry Clerk Job Description

A Data Entry Clerk keeps an organization's information systems accurate: databases, spreadsheets, customer records, financial systems, and operational platforms all depend on someone keying source information in correctly the first time. The task sounds simple, but the cost of doing it poorly is not. A transposed account number delays a payment, a wrong address misroutes a shipment, and an incorrect date on a record can create a compliance problem that surfaces months later.

The core cycle is the same everywhere: receive source information (paper forms, scanned files, emails, or verbal instructions), interpret it correctly, enter it into the designated system, and verify accuracy before moving on. Volume and format vary by employer. A healthcare clerk might process patient intake forms all day, each with specific formatting for names, dates of birth, insurance IDs, and diagnosis codes. A logistics clerk might key shipment details, origins, destinations, weights, item counts, from carrier invoices into a transportation management system. State job classification specs for this role (Colorado's G2D series, Ohio's Data Entry series 1233) describe the same core: locating data on source documents, coding and entering it, verifying accuracy, and correcting errors, and at higher levels ensuring data transfers correctly from one system to another.

Checking your own work matters as much as the initial entry. Batch verification, comparing a completed set of entries against source documents to catch transpositions, wrong-field entries, and missing information, is standard practice anywhere data integrity matters. Clerks who catch their own errors before submission are worth more than clerks who submit errors and let them surface downstream.

Many data entry roles also include records management: scanning and filing paper documents into digital archives, maintaining folder structures, and pulling historical records on request. That work calls for organizational skill and familiarity with file naming and version control conventions, not just typing speed.

The judgment component of the role is small but real. When a source document is incomplete or ambiguous, a blank field, an unclear scanned number, the clerk has to resolve the gap rather than guess. That might mean calling the person who submitted the form, flagging the record for supervisor review, or applying a documented protocol for handling common gaps. This exception-handling judgment is part of the job that AI document capture tools, which work best on straightforward, predictable formats, do not replace.

Education

• High school diploma or GED covers the requirement at most employers.
• An associate degree in business administration, office technology, or health information management is preferred at some organizations and required for higher-graded classification levels in state government systems.
• Community college certificate programs in administrative technology or medical billing are accepted in place of a degree at many employers.

Technical skills

• Typing speed of 40 to 60 words per minute is the common baseline; 60 to 80 WPM is competitive for production-focused roles.
• Microsoft Excel: data entry, sorting and filtering, basic formulas, and pivot tables for more advanced positions.
• Microsoft Word and Outlook for routine correspondence.
• Database software: Microsoft Access or an employer's proprietary system; Colorado's classification spec also describes ensuring data transfers correctly from one system to another.
• Document tools: PDF software and scanning systems for records-heavy roles.

Industry-specific skills

• Healthcare: medical terminology, basic ICD-10 familiarity, and EHR platforms such as Epic or Cerner.
• Financial and insurance: account or policy number formats and claims coding basics.
• Logistics and government: ERP navigation (SAP, Oracle) or agency-specific case management systems.

Personal characteristics

• Accuracy under volume pressure. Speed matters less than holding quality steady across a full shift.
• Sustained concentration, since data entry requires focus that does not degrade over long stretches of repetitive work.
• Procedural compliance: following established entry formats and validation steps without improvising around them.
• Discretion, since many roles involve confidential personal, financial, or medical information.

No prior work experience is typically required for entry-level positions; state classification systems place the base data entry level under immediate supervision, with lead and supervisory levels reserved for staff who have already worked the entry-level role. Clerks who add spreadsheet, database, or industry-specific coding skills qualify faster for higher-graded and better-paid classifications.

Hiring signals employers actually screen for

• A timed typing or ten-key test is standard at the application stage, not an afterthought; treat it as the first real evaluation.
• References or work samples that show sustained accuracy over volume, not a single fast burst, carry more weight than a resume line claiming a WPM number.
• Familiarity with the employer's specific systems, an EHR platform in healthcare, an ERP module in logistics, a system-to-system transfer step in government, shortens onboarding and is worth naming directly in an application even without formal certification.

BLS Employment Projections expect employment of Data Entry Keyers (SOC 43-9021) to fall about 25.5 percent from 2025 to 2035, from roughly 131,800 jobs to 98,200, with about 7,700 openings projected per year nationally, all of them from the need to replace workers who leave rather than from new positions. The occupation is not designated a Bright Outlook field. OCR, robotic process automation, and AI document-capture tools are all designed to handle the standardized, high-volume keying that makes up much of entry-level data entry work.

Automation pressure is not the same across every role. It is heaviest in roles built around predictable, repetitive document formats, the kind of work automation handles well. It is lighter where source documents vary, where exceptions are common, and where a human still has to make a judgment call and be accountable for it. A 2026 Stanford Digital Economy Lab study tracking AI's employment effects found that employment among workers ages 22 to 25 in highly AI-exposed occupations generally stood about 19 percent below the pace of same-age peers in less-exposed occupations as of mid-2026, with the declines concentrated where AI automates tasks rather than assisting workers. The study looks at AI-exposed occupations broadly rather than data entry alone, but it points in the same direction as the BLS projection for this occupation, and workers who never move past pure keying carry the most exposure.

Settings where the role continues to exist include healthcare (complex clinical data and exception-heavy medical records), state and local government (the classification systems in Colorado, Ohio, and elsewhere still maintain multi-level data entry series), and small and mid-sized businesses that have not automated their intake processes. In these settings, a clerk who understands the domain and keeps a strong accuracy record still finds steady work.

The more useful lens for someone starting in this role is what comes next. Data entry builds concrete, transferable skills, database familiarity, procedural discipline, and attention to detail, that support a move into administrative coordinator, records management, medical coding and billing, or data quality analyst roles. Government classification systems make this progression explicit: entry-level data entry positions sit below lead-worker and supervisory levels in the same job series, and clerks typically qualify for the next level by demonstrating accuracy and taking on more complex source documents rather than by earning a new credential.

For job seekers, data entry remains one of the more accessible entry points into office and technology work, requiring fewer credentials than most white-collar positions while providing direct exposure to the systems and processes that run an organization. Given the automation pressure on pure keying, the strongest long-term position is to treat the role as a stepping stone and start building adjacent skills from day one rather than staying static in it.

Dear Hiring Manager,

I'm applying for the Data Entry Clerk position at [Company]. I spent the past year working part-time in my college's registrar office while completing my associate degree, and I'm now looking for a full-time role where I can apply the accuracy and records skills I built there.

In the registrar's office, I processed student enrollment forms, grade change requests, and records update submissions, roughly 40 to 60 transactions a day during peak registration and 15 to 20 during quieter terms. The most important part of the job was catching errors before they hit a student's academic record: wrong course numbers, mismatched student IDs, dates that didn't line up with the academic calendar. I built a habit of reading each entry back against the source document before submitting it, which my supervisor said noticeably cut down on correction requests compared to previous student workers.

I type at 65 WPM with about 98 percent accuracy on standard tests. I'm comfortable in Microsoft Excel at an intermediate level, including pivot tables and VLOOKUP, and I used the university's enrollment management system daily for a year. I pick up new software quickly once I have access and a bit of context on how a system fits into the larger workflow.

I'm particularly interested in [Company]'s data entry role because [specific reason: the industry, the chance to grow toward a more specialized function, or similar]. I'm available immediately and can work a standard Monday through Friday schedule.

Thank you for your consideration.

[Your Name]