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Data Preparation Jobs (NOW HIRING)

Perform feature engineering, data preparation, and advanced sampling techniques for large-scale datasets. * Develop geospatial analytics and LiDAR processing solutions using industry-standard tools ...

Perform feature engineering, data preparation, and advanced sampling techniques for large-scale datasets. * Develop geospatial analytics and LiDAR processing solutions using industry-standard tools ...

Expert-level capability in data preparation, cleansing, modeling, optimization, and analysis of complex datasets. * Proficiency with visualization and analytics tools such as Tableau, Power BI, SAS ...

Data Scientist

Falls Church, VA · On-site

$110 - $150/hr

Perform data acquisition, ETL, exploratory data analysis, data preparation, feature engineering, and validation using Python, SQL, and related technologies. * Apply statistical methods, predictive ...

Perform feature engineering, data preparation, and advanced sampling techniques for large-scale datasets. * Develop geospatial analytics and LiDAR processing solutions using industry-standard tools ...

Data Analyst

Denver, CO · On-site

$95 - $110/hr

The ideal candidate is a hands-on, self-directed analyst with strong expertise in SQL, Excel, and Tableau , along with experience using Alteryx for data preparation and transformation. This ...

New

The ideal candidate is a hands-on, self-directed analyst with strong expertise in SQL, Excel, and Tableau , along with experience using Alteryx for data preparation and transformation. This ...

Perform feature engineering, data preparation, and advanced sampling techniques for large-scale datasets. * Develop geospatial analytics and LiDAR processing solutions using industry-standard tools ...

Perform feature engineering, data preparation, and advanced sampling techniques for large-scale datasets. * Develop geospatial analytics and LiDAR processing solutions using industry-standard tools ...

Perform feature engineering, data preparation, and advanced sampling techniques for large-scale datasets. * Develop geospatial analytics and LiDAR processing solutions using industry-standard tools ...

Data Scientist

San Antonio, TX · On-site

$110 - $150/hr

Perform data acquisition, ETL, exploratory data analysis, data preparation, feature engineering, and validation using Python, SQL, and related technologies. * Apply statistical methods, predictive ...

Perform feature engineering, data preparation, and advanced sampling techniques for large-scale datasets. * Develop geospatial analytics and LiDAR processing solutions using industry-standard tools ...

Perform feature engineering, data preparation, and advanced sampling techniques for large-scale datasets. * Develop geospatial analytics and LiDAR processing solutions using industry-standard tools ...

Perform feature engineering, data preparation, and advanced sampling techniques for large-scale datasets. * Develop geospatial analytics and LiDAR processing solutions using industry-standard tools ...

Perform feature engineering, data preparation, and advanced sampling techniques for large-scale datasets. * Develop geospatial analytics and LiDAR processing solutions using industry-standard tools ...

Perform feature engineering, data preparation, and advanced sampling techniques for large-scale datasets. * Develop geospatial analytics and LiDAR processing solutions using industry-standard tools ...

Perform feature engineering, data preparation, and advanced sampling techniques for large-scale datasets. * Develop geospatial analytics and LiDAR processing solutions using industry-standard tools ...

Perform feature engineering, data preparation, and advanced sampling techniques for large-scale datasets. * Develop geospatial analytics and LiDAR processing solutions using industry-standard tools ...

Showing results 41-60

Data Preparation information

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How much do data preparation jobs pay per hour?

As of Aug 20, 2026, the average hourly pay for data preparation in the United States is $25.77, according to ZipRecruiter salary data. Most workers in this role earn between $13.94 and $29.81 per hour, depending on experience, location, and employer.

What is a data preparation?

A Data Preparation job involves collecting, cleaning, structuring, and transforming raw data into a usable format for analysis or machine learning. Professionals in this role ensure data quality by handling missing values, removing duplicates, and standardizing formats. They work with databases, ETL tools, and programming languages like SQL or Python. Data preparation is crucial for accurate analytics, reporting, and AI model performance.

What does a data preparation do?

A Data Preparation professional typically spends their days gathering, cleaning, and organizing raw data from various sources to make it ready for analysis. This involves identifying data discrepancies, standardizing formats, and collaborating with data analysts or engineers to resolve data quality issues. You may also automate parts of the data pipeline, document processes, and troubleshoot issues to ensure data accuracy and reliability. Working closely with cross-functional teams ensures that the data provided meets the needs of different business or research objectives. Mastery of these daily responsibilities enables smoother downstream analysis and a critical foundation for data-driven decision-making.

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

To thrive in Data Preparation, you should possess strong analytical skills, attention to detail, and proficiency in managing large datasets, often supported by a background in statistics, mathematics, or computer science. Familiarity with data wrangling tools such as SQL, Python (pandas), or specialized ETL software is typically required, and certifications in data analytics or data engineering are advantageous. Excellent problem-solving abilities, communication skills, and adaptability stand out in this role. These skills ensure data quality, streamline data workflows, and support effective collaboration with analysts and data scientists to drive organizational insights.

More about Data Preparation jobs

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

The most popular types of Data Preparation jobs are:

What states have the most Data Preparation jobs?

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

Infographic showing various Data Preparation job openings in the United States as of August 2026, with employment types broken down into 73% Full Time, 16% Part Time, and 11% Contract. Highlights an 100% In-person job distribution, with an average salary of $53,606 per year, or $25.8 per hour.

Full-time

Posted 13 days ago


Job description

Required Skills
  • 10+ years of hands-on experience in Applied Data Science, Analytics Engineering, and Systems Modeling.
  • 5+ years of client-facing, consulting, or business development experience delivering analytics solutions.
  • Expertise in statistical modeling, machine learning, and predictive analytics.
  • Strong proficiency with Python, scikit-learn, statsmodels, PyTorch, and TensorFlow.
  • Experience in time-series forecasting, causal inference, feature engineering, and advanced data sampling/resampling techniques.
  • Strong expertise in geospatial analytics and LiDAR data processing.
  • Hands-on experience with ArcGIS, PostGIS, GeoPandas, GDAL/OGR, PDAL, and related geospatial libraries.
  • Experience working with vector, raster, point-cloud, and sensor datasets.
  • Excellent analytical, communication, and stakeholder management skills.
Roles & Responsibilities
  • Design and develop advanced machine learning and statistical models to solve complex business problems.
  • Build predictive models, time-series forecasting solutions, and causal inference frameworks.
  • Perform feature engineering, data preparation, and advanced sampling techniques for large-scale datasets.
  • Develop geospatial analytics and LiDAR processing solutions using industry-standard tools and libraries.
  • Analyze vector, raster, point-cloud, and sensor data to generate actionable insights.
  • Partner with business stakeholders to scope, design, and deliver data science solutions.
  • Present analytical findings and recommendations to technical and business audiences.
  • Optimize model performance, scalability, and deployment in production environments.
  • Mentor data scientists and promote best practices in analytics and machine learning.
  • Support innovation initiatives through advanced analytics and AI-driven solutions.