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Data Processing Jobs in Kentucky (NOW HIRING)

Data Entry Processor Onsite Lexington, KY $15 per hour & Great Benefits About the Data Entry Processor: We are seeking a reliable and self-motivated Data Entry Processor to join our operations team.

Data Entry Processor Onsite Lexington, KY $15 per hour & Great Benefits About the Data Entry Processor: We are seeking a reliable and self-motivated Data Entry Processor to join our operations team.

The Image Data Scientist position serves as the company's technical lead and subject matter expert ... Optimize image-processing algorithms for biological experiments conducted in microgravity.

The Image Data Scientist position serves as the company's technical lead and subject matter expert ... Optimize image-processing algorithms for biological experiments conducted in microgravity.

Data Engineer (Remote)

Louisville, KY · On-site +1

$104K - $125K/yr

Document data structures, processes, architectural decisions, and best practices for knowledge sharing * Develop, maintain, and optimize Snowflake objects (schemas, tables, views) and SQL ...

Data Engineer (Remote)

Louisville, KY · On-site +1

$104K - $125K/yr

Document data structures, processes, architectural decisions, and best practices for knowledge sharing * Develop, maintain, and optimize Snowflake objects (schemas, tables, views) and SQL ...

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Data Processing information

See Kentucky salary details

$10

$17

$30

How much do data processing jobs pay per hour?

As of Jul 11, 2026, the average hourly pay for data processing in Kentucky is $17.60, according to ZipRecruiter salary data. Most workers in this role earn between $13.99 and $19.42 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in the Data Processing position, and why are they important?

To thrive in Data Processing, you need strong analytical abilities, attention to detail, and proficiency with spreadsheets and database management, often supported by an associate's degree or relevant experience. Familiarity with tools like Microsoft Excel, SQL, or data entry software, as well as certifications such as Certified Data Processor (CDP), are frequently expected. Strong organizational skills, time management, and the ability to troubleshoot problems efficiently are valued soft skills. These competencies are crucial for ensuring data accuracy, meeting deadlines, and supporting smooth information operations within an organization.

What is a data processing job role?

A data processing job involves collecting, organizing, and converting raw data into a usable format for analysis or reporting. It often requires skills in data management tools, attention to detail, and knowledge of data formats and software such as Excel, SQL, or specialized processing programs.

What are the typical daily responsibilities of someone working in Data Processing?

A typical day for a Data Processing professional involves entering, validating, and updating records in databases or spreadsheets to ensure data integrity. You may also be responsible for generating reports, cleaning large data sets, and identifying discrepancies or errors for correction. Collaboration with team members or departments is common to clarify data requirements and resolve issues. Staying organized and attentive to detail is essential because the quality of processed data can impact decision-making across the organization.

What is a Data Processing job?

A Data Processing job involves collecting, organizing, and managing data to ensure accuracy and accessibility. Professionals in this role use software tools to input, clean, analyze, and process data for businesses or organizations. They may also generate reports and automate workflows to streamline data handling. Strong attention to detail and proficiency in data management tools are essential for success in this field.

Is AI replacing data entry jobs?

AI is automating many data entry tasks by using machine learning and optical character recognition, which can increase efficiency and reduce manual labor. However, data processing jobs still require human oversight for complex or unstructured data, and roles involving data validation, analysis, and management remain essential. Professionals in data processing should develop skills in AI tools and data management to stay relevant.

What is the highest paying job in data?

In data-related fields, roles such as Data Science Director, Chief Data Officer, or Senior Data Architect tend to have the highest salaries, often exceeding six figures annually. These positions typically require advanced skills in data analysis, machine learning, and leadership, along with extensive experience and relevant certifications.
What are the most commonly searched types of Data Processing jobs in Kentucky? The most popular types of Data Processing jobs in Kentucky are:
What are popular job titles related to Data Processing jobs in Kentucky? For Data Processing jobs in Kentucky, the most frequently searched job titles are:
What cities in Kentucky are hiring for Data Processing jobs? Cities in Kentucky with the most Data Processing job openings:
Infographic showing various Data Processing job openings in Kentucky as of July 2026, with employment types broken down into 1% As Needed, 81% Full Time, 15% Part Time, and 3% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $36,611 per year, or $17.6 per hour.
Senior Data Modeler

Full-time

Re-posted 10 days ago


BrightSpring Health Services rating

4.8

Company rating: 4.8 out of 10

Based on 62 frontline employees who took The Breakroom Quiz

215th of 234 rated social care providers


Job description

Our Company

BrightSpring Health Services

Overview

We are seeking a highly skilled Senior Data Modeler to join our Data Engineering & Architecture team. This role will play a critical part not only in designing, developing, and maintaining logical and physical data models, but also in architecting, building, and optimizing the data pipelines and platforms that power our enterprise data warehouse, analytics ecosystem, and business intelligence solutions. This position ensures that data assets are structured, engineered, and delivered in a scalable, high performance, and user-friendly manner across the organization.

Responsibilities
  • Design, implement, and optimize conceptual, logical, and physical data models to support enterprise reporting, analytics, and data science use cases.
  • Collaborate with data engineers, business analysts, and business stakeholders to translate business requirements into robust data structures.
  • Define and enforce data modeling standards, best practices, and naming conventions across the organization.
  • Develop and maintain data dictionaries, ER diagrams, and metadata documentation to ensure clarity and consistency.
  • Analyze existing data models and workflows to identify opportunities for improvement in performance, scalability, and maintainability.
  • Contribute to the development of enterprise data architecture patterns and reusable modeling frameworks.
  • Architect, build, and optimize scalable ETL/ELT pipelines using modern data engineering frameworks and cloud technologies.
  • Lead the design and development of distributed data processing workflows using Databricks, PySpark, Azure SQL and/or Azure Synapse.
  • Develop and optimize data ingestion frameworks (batch and streaming) from diverse sources including FHIR, APIs, files, databases, and event streams.
  • Ensure data pipelines meet enterprise standards for performance, reliability, observability, and recoverability.
  • Perform advanced SQL, PySpark, or Python optimization to maximize query speed and dataset availability for analytics and downstream applications.
  • Oversee data lake and data warehouse architecture, including partitioning strategies, delta lake management, schema evolution, and performance tuning.
  • Troubleshoot, diagnose, and resolve complex data engineering and pipeline issues across cloud environments.
  • Mentor junior engineers and modelers, influencing engineering patterns, coding standards, and architectural direction.
  • Collaborate with security teams to implement proper access controls, encryption, secrets management, and compliance processes.
Qualifications
  • Bachelor's degree in Computer Science, Information Systems, Data Management, or related field (or equivalent experience).
  • 7-10 years of experience in data modeling, data engineering, dimensional modeling, or data architecture roles.
  • Strong knowledge of relational, dimensional, and NoSQL data modeling techniques.
  • Advanced SQL skills and experience designing for cloud data platforms (Databricks, Synapse, Azure SQL Databases, Redshift, BigQuery, or similar).
  • Expertise in building scalable ETL/ELT processes using modern data engineering tools (Azure Data Factory, Databricks, Synapse Pipelines, SSIS, etc.).
  • Strong proficiency with Python, PySpark, or Scala for data engineering and scripting.
  • Hands-on experience with Azure cloud data services: Azure Data Factory, Azure SQL Database, Azure Synapse Analytics, Azure Data Lake Storage Gen2, Databricks.
  • Experience designing and optimizing data lakes, delta lakehouse architectures, and large-scale distributed data systems.
  • Experience working with DevOps concepts-CI/CD pipelines, Git branching strategies, automated testing, and deployment.
  • Ability to orchestrate and influence remote teams, ensuring successful implementation of complex data solutions.
  • Detail-oriented with excellent organizational skills.
  • Effective working in a cross-functional, dynamic, and remote environment.
  • Strategic thinker with the ability to balance short-term deliverables with long-term platform evolution.

Preferred

  • Hands-on experience designing, building, and operationalizing unified data platforms, including semantic layers, ontologies, and knowledge graphs, to enable AI/ML product development.
  • Experience with enterprise-scale analytics environments and BI tools (Power BI, Qlik, Tableau, Databricks AI/BI Dashboards).
  • Exposure to data governance, data cataloging, and MDM practices.
  • Knowledge of data vault modeling, star schema, and snowflake modeling.
  • Experience designing real-time/streaming data pipelines (Kafka, Event Hubs, Spark Streaming, etc.).
  • Familiarity with API platforms and tools such as Postman or API gateways.
  • Experience tuning large-scale Spark workloads and optimizing cloud compute costs.
  • Strong communication and collaboration skills across both technical and non-technical teams.

Key Competencies

  • Analytical and meticulous mindset with a strong ability to solve complex data design and engineering challenges.
  • Ability to balance short-term deliverables with long-term enterprise strategy.
  • Strong documentation and communication skills for presenting technical concepts to non-technical audiences.
  • Leadership qualities with the ability to mentor and guide junior team members.
  • Ability to think holistically across data modeling, data engineering, and data architecture disciplines.
About our Line of BusinessBrightSpring Health Services provides complementary home- and community-based health solutions for complex populations in need of specialized and/or chronic care. Through the Company's service lines, including pharmacy, home health care, and rehabilitation, we provide comprehensive and more integrated care and clinical solutions in all 50 states to over 475,000 customers, clients and patients daily. BrightSpring has consistently demonstrated strong and industry-leading quality metrics across its services lines, while improving the health and quality of life for high-need individuals and reducing overall healthcare system costs. For more information, please visit www.brightspringhealth.com. Follow us on Facebook, LinkedIn, and X.Employment Type: FULL_TIME

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