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On Call Data Pipeline Engineer Jobs (NOW HIRING)

$110 - $120/hr

Build and manage the AWS cloud infrastructure, CI/CD pipelines, serverless data ingestion, and observability platform for CQRE One Portal -- ensuring the application is securely deployed, scalable ...

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On Call Data Pipeline Engineer information

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How much do on call data pipeline engineer jobs pay per year?

As of Sep 4, 2026, the average yearly pay for on call data pipeline engineer in the United States is $147,461.00, according to ZipRecruiter salary data. Most workers in this role earn between $84,000.00 and $196,000.00 per year, depending on experience, location, and employer.

What are the most commonly searched types of Data Pipeline Engineer jobs?

The most popular types of Data Pipeline Engineer jobs are:

MLOps Data Pipeline Engineer (Airflow & MLflow) - Q125

R2 Technologies Corporation

Alpharetta, GA • On-site

$111K - $134K/yr

Full-time

Medical, Retirement, PTO

Re-posted yesterday


Job description

Overview:
R2 Technologies Corporation (R2), headquartered in Alpharetta, GA, is a leading IT services provider specializing in Java, .NET, Big Data, Cloud Computing (AWS, GCP, Azure), Artificial Intelligence (AI), Machine Learning (ML), software development, project management, SAP, and enterprise resource planning (ERP). We empower clients-from startups to Fortune 1000 companies-with scalable, platform-based solutions and data-driven insights using modern cloud technologies. Our commitment to blending highly skilled talent with innovative productivity platforms ensures rapid delivery of business value, making us one of the most respected and trusted technology companies in the United States. At R2, we're passionate about driving operational excellence and competitive advantage for our clients through cutting-edge AI, ML, and cloud solutions. Join our team and help shape the future of technology innovation!
MLOps Data Pipeline Engineer (Airflow & MLflow)
Location: Alpharetta, GA (willing to travel to client locations)
Employment Type: Full-Time (W2)
Role Overview
We are seeking a skilled MLOps Data Pipeline Engineer to build and manage machine learning pipelines using Airflow and MLflow. This role focuses on integrating Spark or Python-based data workflows for efficient model training and deployment.
Key Responsibilities
  • Design and implement machine learning pipelines using Airflow for orchestration and MLflow for model management.
  • Develop data workflows with Spark or Python to preprocess and feed data into ML models.
  • Automate MLOps processes for model training, validation, and deployment using Kubeflow or similar tools.
  • Collaborate with data scientists to monitor and optimize ML pipeline performance and accuracy.
  • Ensure data pipeline scalability, reliability, and governance in production environments.
  • Troubleshoot and resolve issues in data workflows to maintain seamless ML operations.

Required Qualifications
  • Bachelor's degree in Computer Science, Software Engineering, or a related field (or equivalent experience).
  • 3 years of experience as a Data Engineer with a focus on MLOps and machine learning pipelines.
  • Proficiency in using Airflow for pipeline orchestration and MLflow for model lifecycle management.
  • Experience with Spark or Python for building scalable data workflows in ML environments.
  • Strong understanding of MLOps practices and their integration into data engineering pipelines.

Preferred Qualifications
  • Familiarity with Kubeflow for advanced MLOps workflows and Kubernetes-based deployments.
  • Exposure to cloud platforms like AWS or GCP for hosting MLOps pipelines.
  • Knowledge of data versioning tools like DVC for managing ML datasets and models.

Compensation & Benefits
  • Competitive salary and comprehensive benefits package (healthcare, PTO, 401k).
  • Opportunities for professional growth and upskilling in AI and cloud technologies.

R2 Technologies Corporation is an equal opportunity employer and values diversity in the workplace.
Skills:
Data Engineer, MLOps, Airflow, MLflow, Kubeflow, Spark, Python, Machine Learning Pipelines