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Data Cleaner Jobs in Texas (NOW HIRING)

Experience in setting up supervised unsupervised learning ML/NLP models including data cleaning, data analytics, feature creation, model selection ensemble methods, performance metrics visualization.

Clean, transform, and prepare large, complex datasets for modeling and analysis. * Work with diverse data sources including business credit bureau data, bank transaction data, financial statements ...

Clean, transform, and prepare large, complex datasets for modeling and analysis. * Work with diverse data sources including business credit bureau data, bank transaction data, financial statements ...

SQL, data cleaning, feature engineering * Hyperscale's: AWS, Azure, or GCP experience a plus * On prem and cloud development projects with CI/CD Experience * Design, develop, and implement advanced ...

High proficiency and professional experience with data acquisition, data manipulation, data cleaning. * Interview process: In-person interview mandatory and there will be 2 rounds and both are In ...

Clean, manipulate, and analyze large datasets to ensure data quality and integrity. Create and maintain dashboards and reports in Power BI or other data visualization tools to communicate complex ...

High proficiency and professional experience with data acquisition, data manipulation, data cleaning. * Interview process: In-person interview mandatory and there will be 2 rounds and both are In ...

Experience in setting up supervised unsupervised learning ML/NLP models including data cleaning, data analytics, feature creation, model selection ensemble methods, performance metrics visualization

Coordinate acquisition, cleaning, merging, and management of data from multiple secondary sources (local, state, and national databases) and ensure robust data governance and compliance with relevant ...

Coordinate acquisition, cleaning, merging, and management of data from multiple secondary sources (local, state, and national databases) and ensure robust data governance and compliance with relevant ...

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Data Cleaner information

See Texas salary details

$7

$21

$60

How much do data cleaner jobs pay per hour?

As of Jul 5, 2026, the average hourly pay for data cleaner in Texas is $21.11, according to ZipRecruiter salary data. Most workers in this role earn between $11.42 and $21.99 per hour, depending on experience, location, and employer.

What are Data Cleaners?

Data Cleaners are professionals who specialize in identifying and correcting errors, inconsistencies, and inaccuracies in datasets to ensure the information is accurate, complete, and ready for analysis. Their work involves tasks such as removing duplicate records, filling in missing values, standardizing formats, and validating data against predefined rules. Data Cleaners play a crucial role in maintaining data quality, which is essential for reliable data analysis and decision-making in organizations.

What are some typical challenges faced by Data Cleaners when working with large datasets, and how can they be addressed?

Data Cleaners often encounter challenges such as inconsistent data formats, missing values, and duplicate records, especially when handling large datasets from multiple sources. Addressing these issues requires a solid understanding of data validation techniques and the use of specialized tools or programming languages like Python or SQL. Collaboration with data analysts or database administrators is also essential to clarify data requirements and resolve complex discrepancies, ensuring the cleaned data meets the organization's standards for analysis and reporting.

What are the key skills and qualifications needed to thrive as a Data Cleaner, and why are they important?

To excel as a Data Cleaner, you need a solid understanding of data management principles, attention to detail, and proficiency in data analysis, often supported by a degree in a quantitative field. Familiarity with data cleaning tools such as Microsoft Excel, Python (Pandas), SQL, and sometimes specialized ETL software is typically required. Strong problem-solving skills, patience, and the ability to communicate data issues clearly set outstanding Data Cleaners apart. These skills ensure that data is accurate, consistent, and reliable, which is crucial for effective analysis and business decision-making.

What is the difference between Data Cleaner vs Data Analyst?

AspectData CleanerData Analyst
Required CredentialsHigh school diploma or equivalent; some roles may require basic certificationsBachelor's degree in data science, statistics, or related field
Work EnvironmentData processing centers, offices, remoteOffices, remote, or client sites
Industry UsageData management, IT, business servicesBusiness intelligence, finance, marketing, healthcare
Common Search/ComparisonOften compared for entry-level data rolesMore analytical, interpretative roles

Data Cleaners focus on preparing raw data by removing errors and inconsistencies, ensuring data quality. Data Analysts interpret cleaned data to generate insights and support decision-making. While both roles work with data, Data Cleaners handle data preparation, whereas Data Analysts analyze data to provide strategic recommendations.

What cities in Texas are hiring for Data Cleaner jobs? Cities in Texas with the most Data Cleaner job openings:

Data Scientist

Sarian, Inc.

Addison, TX • On-site

Full-time

Posted 5 days ago


Job description

JD:
  • 7+ yrs of experience as data scientist or related roles.
  • Deep understanding and some exposure to new Gen AI open-source models.
  • Atleast 5 years of experience in software development and agile process.
  • Atleast 5 years python (or equivalent) programming experience to work with ML/NLP models.
  • Experience in setting up supervised unsupervised learning ML/NLP models including data cleaning, data analytics, feature creation, model selection ensemble methods, performance metrics visualization.
  • Experience in ML/NLP development pipelines of large datasets, both structured unstructured.
  • Atleast 2 years experience in designing and developing enterprise scale ML/NLP solutions in one or more of: Named entity recognition, document classification, document summarization, topic modelling, dialog systems, sentiment analysis, ocr text processing.
  • Knowledge and hands-on experience working with OCR products.
  • Primary skills: Data scientist, Secondary skills: Artificial intelligence/Machine learning.