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Full Time Remote Big Data Jobs (NOW HIRING)

The Data Sherpas

Data Engineer - GCP

Atlanta, GA · On-site +1

$110K - $132K/yr

The ideal candidate will bring strong expertise in cloud-based data processing, big data ... Flexible work environment and remote work options. Join us and be part of a team building ...

Radiance Technologies

Data Analyst

Advanced Trade Analytics Platform (ATAP) Remote (U.S.) Active Top Secret Clearance Required Use ... Five (5) years of experience in applied research, big data analytics, statistics, data science ...

Aretec

AWS Data Analytics and Management Lead

Aretec seeks an AWS Data Analytics and Management Lead for a 100% remote opportunity experience ... enterprise big data application deployment and solution architecture on complex heterogeneous ...

Radiance Technologies

Data Scientist

Remote Work Flexibility * Meaningful National Security Mission * Exposure to Advanced Analytics, AI, and Big Data Technologies * Career Growth and Professional Development * Collaborative and ...

Spokeo

Data Engineering Manager

Pasadena, CA · On-site +1

$172K - $206K/yr

Founded in 2006, Spokeo has built a dedicated, remote-first team with an average tenure of 6.9 ... Expertise in data lakes, warehouses, modeling, ETL, and big data platforms. * Familiarity with ...

Hitachi

AI and Data Engineer

Santa Clara, CA · On-site +1

$29/hr

Profession (Job Category): IT, Telecom & Internet Job Schedule: Full time Remote: No AI and Data ... around big data, and establishing Hitachi as a leader in Industrial AI. Role Overview We are ...

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Full Time Remote Big Data information

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How much do full time remote big data jobs pay per hour?

As of Jun 15, 2026, the average hourly pay for full time remote big data in the United States is $62.98, according to ZipRecruiter salary data. Most workers in this role earn between $53.61 and $70.91 per hour, depending on experience, location, and employer.

What is the difference between Full Time Remote Big Data vs Full Time Remote Data Engineer?

AspectFull Time Remote Big DataFull Time Remote Data Engineer
Required CredentialsBachelor's in CS, Data Science, or related; experience with Hadoop, SparkBachelor's in CS, Software Engineering; experience with ETL, cloud platforms
Work EnvironmentRemote, collaborative teams, data analysis focusRemote, development-focused, infrastructure setup
Industry UsageTech, finance, healthcare, retailTech, finance, consulting, e-commerce

Full Time Remote Big Data roles focus on managing and analyzing large datasets using tools like Hadoop and Spark, often emphasizing data processing and analytics. Full Time Remote Data Engineers build and maintain data pipelines, focusing on infrastructure, ETL processes, and cloud platforms. While both roles require similar technical credentials and often operate remotely, their core responsibilities differ: Big Data roles analyze data, whereas Data Engineers develop the systems that enable data analysis.

How can I make 2000 a week working from home?

A full-time remote Big Data professional can earn $2,000 or more weekly by leveraging high-demand skills such as data engineering, analytics, and proficiency with tools like Hadoop or Spark. Achieving this income typically requires extensive experience, specialized certifications, and working on complex projects for clients or employers who pay competitive rates.

How can I make $100,000 a year working from home?

A full-time remote Big Data professional can reach a $100,000 annual salary by gaining expertise in data engineering, analytics, or machine learning, often requiring skills in SQL, Python, and cloud platforms like AWS or Azure. Building a strong portfolio, obtaining relevant certifications, and gaining experience in high-demand areas can help achieve this income level while working remotely.

Is 30 too late for data science?

For a full-time remote big data role, age is generally not a barrier; many professionals transition into data science or big data roles at various ages. Success depends on skills, experience, and continuous learning in tools like Hadoop, Spark, and Python. Age should not deter pursuing a career in data science or big data fields.

What jobs can I do 100% remotely?

Full Time Remote Big Data roles typically involve data analysis, data engineering, or data science positions that can be performed entirely online. These jobs often require skills in programming, cloud platforms, and data management tools, and they allow employees to work from any location with internet access.
More about Full Time Remote Big Data jobs
What cities are hiring for Full Time Remote Big Data jobs? Cities with the most Full Time Remote Big Data job openings:
What are the most commonly searched types of Remote Big Data jobs? The most popular types of Remote Big Data jobs are:
What job categories do people searching Full Time Remote Big Data jobs look for? The top searched job categories for Full Time Remote Big Data jobs are:
Full Time Remote Big Data jobs near you
Infographic showing various Full Time Remote Big Data job openings in the United States as of June 2026, with employment types broken down into 1% As Needed, 82% Full Time, 6% Part Time, and 11% Contract. Highlights an 94% Physical, 2% Hybrid, and 4% Remote job distribution, with an average salary of $131,001 per year, or $63 per hour.

Data Engineer - GCP

The Data Sherpas

Atlanta, GA • On-site, Remote

$110K - $132K/yr

Full-time

Medical, Dental, Vision

Posted 5 days ago

Job description

Who We Are:

We are a dynamic team focused on building innovative and scalable data solutions on Google Cloud Platform (GCP). Our Google Cloud Data Engineer will play a key role in designing, developing, and managing scalable data pipelines and data infrastructure, ensuring data availability, accuracy, and performance for business insights and machine learning models.


What We Are Looking For:

We are seeking an experienced and highly skilled Google Cloud Data Engineer who will be responsible for developing and managing data pipelines on GCP. The ideal candidate will bring strong expertise in cloud-based data processing, big data technologies, and data modeling to help us provide high-performance data solutions.


Responsibilities:

Data Pipeline Development and Management:

  • Design, build, and maintain scalable and reliable data pipelines using Cloud Dataflow, Cloud Pub/Sub, and Cloud Composer.
  • Develop ETL/ELT processes to process and transform large volumes of structured and unstructured data.
  • Optimize data pipeline performance, scalability, and reliability.
  • Ensure data processing and ingestion workflows are monitored and meet performance SLAs.

Data Storage and Management:

  • Design and implement data storage solutions using BigQuery, Cloud Storage, and Firestore.
  • Optimize data structures and partitioning for performance and cost efficiency.
  • Ensure data security, integrity, and availability in all storage solutions.
  • Manage data lifecycle policies and archiving processes.

Data Transformation and Processing:

  • Develop data transformation processes using BigQuery, Apache Beam, and Cloud Functions.
  • Implement data quality checks, validation rules, and monitoring solutions.
  • Support real-time and batch data processing needs.

Data Integration and Automation:

  • Integrate data from multiple sources, including APIs, databases, and third-party applications.
  • Automate data ingestion, transformation, and export using tools like Cloud Composer and Cloud Functions.
  • Ensure data consistency across different environments and systems.

Collaboration and Stakeholder Engagement:

  • Work closely with data scientists and analysts to understand data needs and business goals.
  • Provide technical guidance and best practices to the data engineering and business teams.
  • Collaborate with security and compliance teams to ensure data governance standards are met.

Performance Monitoring and Troubleshooting:

  • Monitor data pipeline performance and troubleshoot issues in real-time.
  • Analyze data pipeline failures and implement fixes to prevent recurrence.
  • Set up logging and monitoring using Stackdriver and Cloud Monitoring.


Qualifications:

  • Bachelor's degree in Computer Science, Data Engineering, or a related field; Master's degree is a plus.
  • 3+ years of experience in data engineering, with at least 2+ years working with Google Cloud Platform.
  • Google Professional Data Engineer certification is required.
  • Strong proficiency with GCP services such as BigQuery, Cloud Dataflow, Cloud Composer, Cloud Pub/Sub, Firestore, and Cloud Functions.
  • Hands-on experience with big data tools and frameworks such as Apache Beam, Hadoop, Spark, or Flink.
  • Proficiency in programming languages such as Python, Java, or Scala.
  • Strong knowledge of SQL, data modeling, and query optimization.
  • Experience with CI/CD tools and version control (e.g., Git, Cloud Build).
  • Strong understanding of data governance, security, and compliance requirements.
  • Ability to manage large-scale data processing and real-time data pipelines.
  • Excellent problem-solving, analytical, and communication skills.


Preferred Skills:

  • Experience with machine learning pipelines and AI/ML model deployment.
  • Familiarity with Terraform and Infrastructure as Code (IaC) principles.
  • Experience with NoSQL databases and key-value stores on GCP.
  • Knowledge of containerization and orchestration using Google Kubernetes Engine (GKE).


What We Offer:

  • Competitive salary and performance-based incentives.
  • Comprehensive health, dental, and vision coverage.
  • Professional development and training opportunities (including GCP certification).
  • Flexible work environment and remote work options.


Join us and be part of a team building innovative and scalable data solutions on Google Cloud Platform!


This position is open to multiple engagement models, including Permanent/Full-Time, Contract, or Corp-to-Corp (C2C) arrangements. We are looking for the best talent and are flexible on the employment structure for the right candidate.


We cannot work with third-party agencies at this time. Resumes submitted via unapproved agencies will be automatically rejected.

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