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

Arva Intelligence

Data Engineer

Houston, TX · On-site +1

$95K - $130K/yr

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 ...

Arva Intelligence

Data Engineer

Houston, TX · On-site +1

$95K - $130K/yr

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 ...

Metro National Corporation

Data Engineer

Houston, TX · On-site +1

$109K - $131K/yr

Remote candidates will not be considered. We do not Sponsor Visa's SUMMARY: MetroNational is ... We are seeking a skilled Data Visualization & Analytics Engineer to join our data management team.

Giga Energy

BIM Lead, Data Center

Houston, TX · On-site +1

The BIM Lead manages digital construction data, enforces model integrity, and drives the workflows ... Coordinate with architects, structural engineers, MEP engineers, civil engineers, and other ...

Horizontal Talent

Azure Databricks Lead Engineer

Houston, TX · On-site +1

$97K - $128K/yr

This role is ideal for a hands-on data engineer who enjoys building scalable solutions directly in ... Lead migration efforts from legacy data platforms into Databricks-based solutions. * Refactor and ...

NATIONMIND LLC

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.

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All JobsLead Data Engineer JobsRemote Lead Data Engineer JobsRemote Lead Data Engineer Jobs in Houston, TX

Remote Lead Data Engineer information

See Houston, TX salary details

$40.6K

$118.2K

$172.4K

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

As of Aug 6, 2026, the average yearly pay for remote lead data engineer in Houston, TX is $118,211.00, according to ZipRecruiter salary data. Most workers in this role earn between $97,900.00 and $128,900.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a remote lead data engineer?

To thrive as a Remote Lead Data Engineer, you need advanced expertise in data architecture, ETL processes, and programming languages such as Python or Scala, typically supported by a degree in computer science or a related field. Familiarity with cloud platforms (like AWS, Azure, or GCP), big data frameworks (such as Spark or Hadoop), and relevant certifications are highly valued. Strong leadership, communication, and problem-solving skills help you effectively manage distributed teams and collaborate cross-functionally. These skills ensure robust data solutions, seamless team coordination, and the ability to deliver scalable analytics infrastructure remotely.

How does a remote lead data engineer typically collaborate with cross-functional teams while working remotely?

As a Remote Lead Data Engineer, collaboration with cross-functional teams—such as data scientists, analysts, product managers, and software engineers—is often facilitated through virtual meetings, project management tools, and shared documentation platforms. Effective communication is crucial, as you’ll be responsible for aligning data architecture with business goals and ensuring that stakeholders are regularly updated on project progress. Many organizations use agile methodologies to structure work, which means you’ll participate in regular stand-ups, sprint planning, and reviews with distributed teams. Building strong relationships and maintaining transparency are key to overcoming remote collaboration challenges and driving project success.

What is a remote lead data engineer?

A Remote Lead Data Engineer is a senior-level professional responsible for designing, building, and maintaining large-scale data systems while working remotely. They oversee data engineering teams, establish best practices, and ensure data pipelines are efficient and reliable. This role combines hands-on technical tasks with leadership responsibilities, such as mentoring junior engineers and collaborating with other departments. Remote Lead Data Engineers must be adept at communication and project management to coordinate effectively with distributed teams.
What are the most commonly searched types of Lead Data Engineer jobs in Houston, TX? The most popular types of Lead Data Engineer jobs in Houston, TX are:
What are popular job titles related to Remote Lead Data Engineer jobs in Houston, TX? For Remote Lead Data Engineer jobs in Houston, TX, the most frequently searched job titles are:
What job categories do people searching Remote Lead Data Engineer jobs in Houston, TX look for? The top searched job categories for Remote Lead Data Engineer jobs in Houston, TX are:
What cities near Houston, TX are hiring for Remote Lead Data Engineer jobs? Cities near Houston, TX with the most Remote Lead Data Engineer job openings:
Remote Lead Data Engineer jobs near you
Infographic showing various Remote Lead Data Engineer job openings in Houston, TX as of August 2026, with employment types broken down into 70% Full Time, 18% Part Time, and 12% Contract. Highlights an 6% In-person, and 94% Remote job distribution, with an average salary of $118,211 per year, or $56.8 per hour.

Data Engineer

Arva Intelligence

Houston, TX • On-site, Remote

$95K - $130K/yr

Full-time

This job post has expired 1 day ago. Applications are no longer accepted.

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

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