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Remote Data Modeler Jobs in Texas (NOW HIRING)

Lockheed Martin

Data Analyst Manager

Fort Worth, TX · On-site +1

... modeling and visualization capabilities to drive business insights and support digital ... What's In It For You From onsite to remote, we offer flexible work schedules to comprehensive ...

XTG

Data Engineer

Dallas, TX · On-site +1

$113K - $136K/yr

... models that support enterprise reporting, analytics, data migration, and operational decision ... This opportunity is remote with the ideal candidate being located in DFW, Phoenix, Atlanta or ...

XTG

Data Engineer

Dallas, TX · On-site +1

$113K - $136K/yr

... models that support enterprise reporting, analytics, data migration, and operational decision ... This opportunity is remote with the ideal candidate being located in DFW, Phoenix, Atlanta or ...

MedReview

Data Scientist

Austin, TX · On-site +1

... models. This position will sit in Austin, Texas. However, for the right fit, we may consider remote ... Data Collection & Preparation: Gather data from various sources (SQL databases, APIs, web scraping ...

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How much do remote data modeler jobs pay per hour?

As of Jun 30, 2026, the average hourly pay for remote data modeler in Texas is $54.70, according to ZipRecruiter salary data. Most workers in this role earn between $49.04 and $63.61 per hour, depending on experience, location, and employer.

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

A Remote Data Modeler should possess strong skills in data modeling concepts, database design, and a background in computer science or a related field. Expertise in tools such as ER/Studio, SQL, and familiarity with cloud data platforms (e.g., AWS, Azure) and relevant certifications like CDMP are highly valued. Exceptional analytical thinking, communication, and self-management abilities set top performers apart, especially when collaborating with distributed teams. These skills enable the creation of accurate, scalable data models and ensure effective remote collaboration on complex data projects.

What does a typical day look like for a Remote Data Modeler?

A typical day for a Remote Data Modeler involves collaborating with stakeholders to gather data requirements, designing and updating data models, and documenting structures for existing or new systems. You’ll spend significant time working with modeling tools, writing or reviewing database scripts, and participating in virtual meetings to ensure alignment with development teams and business analysts. Regular tasks include data mapping, troubleshooting modeling issues, and updating data dictionaries. The role requires balancing focus time for deep analysis with clear virtual communication to ensure projects progress smoothly.

What is a Remote Data Modeler job?

A Remote Data Modeler is responsible for designing, implementing, and optimizing data models that support business intelligence, analytics, and database management. They work with large datasets, ensuring data is structured efficiently for performance and scalability. This role often involves collaboration with data engineers, analysts, and business stakeholders to define data requirements. Since it's a remote position, strong communication and self-management skills are crucial for success.

What are the most commonly searched types of Data Modeler jobs in Texas? The most popular types of Data Modeler jobs in Texas are:
What are popular job titles related to Remote Data Modeler jobs in Texas? For Remote Data Modeler jobs in Texas, the most frequently searched job titles are:
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Remote Data Modeler jobs near you
Data Scientist - Remote

Data Scientist - Remote

NAVA Software Solutions

Houston, TX • On-site, Remote

Full-time

Posted 28 days ago

Job description

NAVA Software solutions is looking for a Data Scientist
Details:
Data Scientist
Location: Houston TX - Remote is ok
Duration: 12 months
Clients want a data scientist who can develop machine learning models to run forecast scenarios. Also, they want this person to be knowledgeable in AWS Cloud technology
A Data Scientist with physical pipeline experience typically specializes in analyzing and optimizing physical infrastructure pipelines, such as those used in the oil and gas industry or transportation networks. Here are some common job duties associated with this role:
  • Data collection and integration: Data scientists with physical pipeline experience gather data from various sources related to the infrastructure pipelines, such as sensors, SCADA (Supervisory Control and Data Acquisition) systems, or IoT devices. They integrate and consolidate the data for analysis and modeling.
  • Pipeline performance analysis: These professionals analyze the performance of physical pipelines by examining data related to flow rates, pressure levels, temperature, corrosion, and other relevant factors. They use statistical techniques and machine learning algorithms to identify patterns, anomalies, and potential issues that may affect pipeline operations.
  • Predictive modeling and maintenance optimization: Data scientists develop predictive models to forecast pipeline performance and detect potential failures or maintenance needs. They utilize historical data, sensor measurements, and other relevant parameters to train models that can predict future events, such as leaks, blockages, or equipment failures. By identifying critical maintenance requirements in advance, they can optimize maintenance schedules and minimize downtime.
  • Risk assessment and mitigation: Data scientists assess risks associated with physical pipelines, such as environmental hazards, security threats, or regulatory compliance. They develop risk assessment models and analyze the impact of different factors on pipeline safety and integrity. Based on these analyses, they propose mitigation strategies to minimize risks and ensure compliance with safety regulations.
  • Optimization of pipeline operations: Data scientists work on optimizing the operational efficiency of physical pipelines. They analyze data to identify areas of improvement, such as reducing energy consumption, optimizing transportation routes, or improving overall system performance. By applying data-driven approaches and algorithms, they provide recommendations to optimize pipeline operations and maximize efficiency.
  • Visualization and reporting: Data scientists with physical pipeline experience create visualizations, reports, and dashboards to communicate their findings and recommendations effectively. They present complex data in a visually understandable format, allowing stakeholders to make informed decisions regarding pipeline maintenance, operations, and risk management.
  • Collaboration with cross-functional teams: These professionals collaborate with engineers, domain experts, operations personnel, and other stakeholders involved in managing physical pipelines. They work together to understand the specific requirements, constraints, and challenges associated with the infrastructure. Effective communication and teamwork are essential to ensure alignment and successful implementation of data-driven solutions.
  • Continuous improvement and innovation: Data scientists keep up with the latest advancements in data science, machine learning, and pipeline technologies. They explore new methodologies, algorithms, and tools to enhance their skills and propose innovative solutions to address pipeline-related challenges.
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About NAVA Software Solutions

Sourced by ZipRecruiter

NAVA is a strategic partner for companies seeking to develop or customize software and products. Our team of experts leverages cutting-edge technology and deep industry knowledge to provide customized solutions that drive business success. Whether you're looking to improve your operations, increase efficiency, or bring a new product to market, NAVA has the expertise and resources to help you achieve your goals. Trust us to be your partner in software and product development.

Industry

It services

Company size

51 - 200 Employees

Headquarters location

Rocky Hill, CT, US

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