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Data Cleaning Jobs (NOW HIRING)

Perform data cleaning, transformation, feature engineering, and exploratory data analysis. * Develop data visualizations, dashboards, and reports. * Evaluate model performance and improve analytical ...

Data Entry Specialist

$17.50 - $23.25/hr

Formatting inconsistencies Data Cleaning & Maintenance * Standardize datasets using: * Filters * Pivot tables * Formulas * Apply consistent: * Naming conventions * Categories * Data structures

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

Data Engineer

Muskegon, MI · On-site

$103K - $124K/yr

... cleaning requirements, and bias - using statistics as necessary Qualifications: +Bachelor's degree in engineering, Data Analysis, Data Science, Computer Science, or Information Systems +Must have ...

Experience using SQL for acquiring and transforming real-world data, data cleaning, data collection or other data wrangling challenges Thanks & Regards Praveen Megan Soft, Inc. Direct No: +1(248) 266 ...

Produce data cleaning, basic analysis, and preparation for visualization * Develop dashboards and perform routine data quality checks * Perform data preprocessing, data integration, quality checks ...

Data Operations Analyst

Manhattan, NY · On-site

$75 - $110/hr

Day-to-day operations including data validation, data entry tasks, data cleaning, security audits and reporting. * Incident management including user communication and reporting for management.

Extract, clean, and organize data from multiple sources, including Google Sheets, Workiva, Looker, and Google Forms. * Partner with the Data Engineering team to consolidate data sources and automate ...

Showing results 41-60

Data Cleaning information

What is a data cleaning?

A Data Cleaning job involves identifying and correcting errors, inconsistencies, and inaccuracies in datasets to ensure high-quality data for analysis. This process includes removing duplicate records, filling in missing values, standardizing formats, and eliminating irrelevant or erroneous data. Data cleaning helps improve data accuracy, reliability, and usability for business intelligence, machine learning, and decision-making. Professionals in this role typically work with databases, spreadsheets, and data management tools to refine raw data into a structured and meaningful format.

What are the key skills and qualifications needed to thrive in data cleaning, and why are they important?

To thrive in Data Cleaning, you need a strong attention to detail, analytical skills, and a solid understanding of data management practices, often supported by training or coursework in data science, statistics, or information technology. Familiarity with tools like Microsoft Excel, SQL, Python (with libraries such as pandas), or specialized data cleaning software is highly valuable. Excellent problem-solving abilities, persistence, and effective communication are important soft skills for identifying and addressing data inconsistencies while collaborating with other team members. These skills are essential to ensure that datasets are accurate, reliable, and ready for analysis, leading to trustworthy business insights.

What are the most common challenges faced by professionals in data cleaning roles?

One of the biggest challenges in data cleaning is dealing with incomplete, inconsistent, or duplicate data from multiple sources, which often requires creative problem-solving and close attention to detail. Communicating with team members to clarify data definitions and intended use is also a frequent part of the job, as misinterpretations can lead to errors. Additionally, deadlines and large datasets can make the role fast-paced, so strong organizational skills and efficiency are important. However, overcoming these challenges offers valuable experience and plays a crucial role in ensuring the success of projects that depend on high-quality data.

What skills are needed for data cleaning?

Data cleaning requires skills in data analysis, attention to detail, and proficiency with tools like Excel, SQL, or data cleaning software. Knowledge of data formats, basic programming (e.g., Python or R), and understanding of data quality principles are also important for effective data cleaning tasks.
More about Data Cleaning jobs

What cities are hiring for Data Cleaning jobs?

Cities with the most Data Cleaning job openings:

What are the most commonly searched types of Data Cleaning jobs?

The most popular types of Data Cleaning jobs are:

What states have the most Data Cleaning jobs?

States with the most job openings for Data Cleaning jobs include:

Infographic showing various Data Cleaning job openings in the United States as of August 2026, with employment types broken down into 83% Full Time, 6% Part Time, and 11% Contract. Highlights an 74% In-person, 6% Hybrid, and 20% Remote job distribution.

Other

Posted 6 days ago


Job description

Summary:
  • Seeking a Data Scientist to analyze complex datasets, develop statistical and machine learning models, and provide data-driven insights to support organizational decision-making.
Responsibilities:
  • Analyze large and complex datasets to identify patterns, trends, relationships, and actionable insights.
  • Develop and implement statistical, predictive, and machine learning models.
  • Perform data cleaning, transformation, feature engineering, and exploratory data analysis.
  • Develop data visualizations, dashboards, and reports.
  • Evaluate model performance and improve analytical solutions.
  • Collaborate with business stakeholders, data engineers, analysts, and technology teams.
  • Translate business problems into data science and analytical solutions.
  • Document methodologies, models, assumptions, and results.
Required Skills:
  • Strong Python and/or R skills.
  • Experience with SQL and relational databases.
  • Knowledge of machine learning, statistics, predictive modeling, and data mining.
  • Experience with Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, or similar technologies.
  • Experience with Power BI, Tableau, or similar visualization tools.
  • Strong analytical, statistical, and problem-solving skills.
  • Experience working with large datasets and cloud/data platforms is preferred.