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Remote Amazon Data Engineer Jobs in Houston, TX (NOW HIRING)

Data Engineer

Houston, TX · On-site +1

$95K - $130K/yr

Remote Base Salary Range: $95k - $130k General Position Description The Data Engineer is responsible for building and scaling the data and computational backbone that supports Arva's ecosystem ...

Data Analyst

Houston, TX · Remote

$40 - $45/hr

Comfortable working with data engineers to validate datasets, metric logic, and dashboard performance. This is a remote position.

Houston, TX 4 days onsite. Fridays are remote. Department: Customer Success, Continuous Improvement ... This role bridges Customer Care Operations, Product, IT, and Data Engineering to translate complex ...

Houston, TX 4 days onsite. Fridays are remote. Department: Customer Success, Continuous Improvement ... This role bridges Customer Care Operations, Product, IT, and Data Engineering to translate complex ...

AI Engineer Developer

Houston, TX · Remote

$40 - $45/hr

Houston TX or US Remote Duration: 6-12 months Skills: AI and Automation~AI Agents Experience ... Azure OpenAI, Amazon Bedrock, Google Vertex AI, Databricks, or similar technologies. · ...

AI Engineer Location: 100% Remote Duration: 6+ month contract-to-hire Requirement: * Implemented ... Build MLOps pipelines for structured/unstructured data. * Implement observability, monitoring, and ...

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Remote Amazon Data Engineer information

See Houston, TX salary details

$42.5K

$123.9K

$169.5K

How much do remote amazon data engineer jobs pay per year?

As of Jul 20, 2026, the average yearly pay for remote amazon data engineer in Houston, TX is $123,876.00, according to ZipRecruiter salary data. Most workers in this role earn between $109,300.00 and $131,300.00 per year, depending on experience, location, and employer.

What does a Remote Amazon Data Engineer do?

A Remote Amazon Data Engineer is responsible for designing, building, and maintaining scalable data pipelines and databases for Amazon or companies using Amazon Web Services (AWS). They work remotely to process large volumes of data, ensure data quality, and enable efficient data analysis. Their tasks typically include extracting data from various sources, transforming it into usable formats, and loading it into data warehouses or analytics platforms. They often use AWS tools such as Redshift, Glue, S3, and Lambda to manage infrastructure and automate workflows. Strong programming skills in languages like Python or SQL are essential for this role.

What is the difference between Remote Amazon Data Engineer vs Remote Amazon Data Analyst?

AspectRemote Amazon Data EngineerRemote Amazon Data Analyst
Required CredentialsBachelor's in CS, Data Engineering certificationsBachelor's in Statistics, Data Analysis certifications
Work EnvironmentDesigning data pipelines, managing ETL processesInterpreting data, creating reports and dashboards
Employer & Industry UsageTech companies, e-commerce, cloud servicesRetail, marketing, e-commerce
Common Search & ComparisonFocus on data infrastructure and pipelinesFocus on data insights and reporting

The main difference between a Remote Amazon Data Engineer and a Remote Amazon Data Analyst lies in their roles. Data Engineers build and maintain data pipelines and infrastructure, requiring technical skills in data architecture. Data Analysts interpret data to generate insights, focusing on analysis and reporting. Both roles are essential in data-driven companies but serve different functions within the data ecosystem.

What are some common challenges faced by Remote Amazon Data Engineers, and how can they be addressed?

Remote Amazon Data Engineers often encounter challenges related to collaborating across time zones and ensuring clear communication with global teams. Effective use of collaboration tools, regular virtual meetings, and clear documentation can help bridge these gaps. Additionally, managing large-scale data pipelines on AWS requires staying updated on best practices for security, scalability, and cost optimization. Proactively participating in team stand-ups and engaging in continuous learning about AWS services can significantly enhance productivity and project outcomes.

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

To thrive as a Remote Amazon Data Engineer, you need strong expertise in data modeling, ETL development, SQL, and programming languages such as Python or Java, typically supported by a degree in computer science or a related field. Familiarity with AWS services like Redshift, S3, Glue, and data pipeline tools, as well as certifications such as AWS Certified Data Analytics, are highly valued. Excellent problem-solving, communication, and self-management skills help remote engineers collaborate effectively and deliver reliable data solutions. These abilities are crucial for ensuring robust, scalable data infrastructure and supporting data-driven decision-making in a distributed work environment.
What are the most commonly searched types of Amazon Data Engineer jobs in Houston, TX? The most popular types of Amazon Data Engineer jobs in Houston, TX are:
What are popular job titles related to Remote Amazon Data Engineer jobs in Houston, TX? For Remote Amazon Data Engineer jobs in Houston, TX, the most frequently searched job titles are:
What job categories do people searching Remote Amazon Data Engineer jobs in Houston, TX look for? The top searched job categories for Remote Amazon Data Engineer jobs in Houston, TX are:
What cities near Houston, TX are hiring for Remote Amazon Data Engineer jobs? Cities near Houston, TX with the most Remote Amazon Data Engineer job openings:

Data Engineer

Arva Intelligence

Houston, TX • On-site, Remote

$95K - $130K/yr

Other

Re-posted 2 days ago


Job description

Job Title:                          Data Engineer 

Department:                     Modeling & Analytics

Reports to:                       Lead Modeling Scientist

Location:                          Remote

Base Salary Range:        $95k - $130k

General Position Description

The Data Engineer is responsible for building and scaling the data and computational backbone that supports Arva's ecosystem modeling and measurement, reporting, and verification platforms. This role sits within a multidisciplinary Data Science team and focuses on designing reliable, auditable, and scalable data systems that enable biogeochemical modeling and optimization at production scale.

In this role, the Data Engineer will design and maintain production-grade data pipelines that integrate diverse datasets including field measurements, management practices, soils, and weather with process-based ecosystem models. The role plays a critical part in ensuring data quality, reproducibility, and traceability so that scientific outputs can be translated into trusted, credit-grade results with real-world impact.

Primary Job Responsibilities

Data Pipeline and Workflow Development

  • Design, implement, and maintain scalable data pipelines supporting ecosystem and biogeochemical modeling
  • Build reproducible workflows that generate standardized model inputs and manage outputs across space, time, and scenario analysis
  • Integrate heterogeneous datasets, including field data, management data, soil data, and weather data, into modeling pipelines

Cloud Infrastructure and Data Systems

  • Develop and maintain cloud-based infrastructure to support modeling pipelines and optimization workflows
  • Implement data storage solutions using relational, spatial, and object-based databases
  • Support efficient data access and processing using platforms such as PostgreSQL, PostGIS, and cloud object storage

Data Quality, Governance, and Auditability

  • Ensure data quality, versioning, traceability, and auditability to support measurement, reporting, and verification requirements
  • Implement validation and monitoring processes to ensure reliability of model inputs and outputs
  • Support transparent, repeatable workflows suitable for regulatory and credit market review

Software Engineering and Collaboration

  • Write clean, modular, and well-documented production code that supports maintainable and scalable data systems
  • Apply software engineering best practices including testing, version control, and documentation
  • Collaborate closely with Data Science and Technology teams to align data infrastructure with modeling, analytics, and production needs

Key Competencies / Requirements

  • 3+ years demonstrated experience building and maintaining data pipelines for large, complex, and heterogeneous datasets
  • Strong proficiency in Python and modern data engineering tools, with experience writing production-grade, testable code
  • Experience working with cloud platforms, with AWS strongly preferred
  • Familiarity with containerization tools such as Docker and version control systems such as GitHub
  • Experience with relational and spatial databases, including PostgreSQL and PostGIS
  • Experience working with geospatial data formats and spatial data processing
  • Experience supporting scientific or ecosystem modeling workflows preferred
  • Familiarity with workflow orchestration tools such as Airflow or Prefect preferred
  • Bachelor's or Master's degree or equivalent experience in Data Engineering, Computer Science, Environmental Informatics, or a related field