OPT Cloud Data Engineer Jobs
Cloud Data Engineer jobs are among the most OPT-friendly roles in tech. Employers regularly sponsor H-1B visa and full-time visas for skilled data engineers, and the specialty occupation classification is well established for this field. Most roles require a degree in computer science, data engineering, or a related field.
Find OPT Cloud Data Engineer JobsOverview
Showing 5 of 15+ Cloud Data Engineer jobs
See all Cloud Data Engineer Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Cloud Data Engineer roles.
Get Access To All JobsINTRODUCTION
We are seeking an ML/AI Data & Cloud Engineer to design, build, and operate the cloud-native components that deliver large-scale, data-intensive AI capabilities.
This role focuses on building scalable data platforms, distributed processing systems, and model-serving infrastructure that enable end-to-end machine learning workflows in production. You will work closely with data scientists and product teams to transform advanced models into reliable, high-performance services.
The ideal candidate combines strong engineering skills with a solid grounding in quantitative methods, and is comfortable leveraging modern AI-assisted development tools to accelerate research, prototyping, and delivery.
Responsibilities
- Design and implement cloud-native data platforms that integrate structured and unstructured data into scalable data lakes or unified data systems
- Build and maintain robust ingestion pipelines using API-driven, streaming, and batch processing workflows
- Architect data processing systems for feature engineering, large-scale computation, and model training support
- Develop and operate model-serving infrastructure enabling real-time and batch inference via APIs
- Develop advanced modeling approaches combining statistical methods and machine learning (e.g., regression, gradient boosting, random forests, and ensemble/meta-learning techniques)
- Implement model validation and monitoring frameworks addressing bias, drift, robustness, and performance
- Tune cloud infrastructure for performance, reliability, and cost efficiency across compute, storage, and data transfer layers
- Build and maintain MLOps pipelines for model deployment, versioning, monitoring, and automated retraining
- Ensure platform reliability, fault tolerance, and data integrity across all stages of the data and model lifecycle
BASIC QUALIFICATIONS
- Bachelor’s degree in Computer Science, Data Engineering, Engineering, Applied Mathematics, or related field (or equivalent experience)
- 3+ years of experience in data engineering, cloud engineering, ML platform engineering, machine learning engineering, AI systems, quantitative modeling, or data-intensive platform development
- Strong programming skills in Python and experience with backend, data engineering, or experience with modern ML frameworks and data tooling
- Experience designing and operating cloud-native systems (AWS or equivalent)
- Experience building and maintaining large-scale data pipelines and working with high-volume datasets
- Experience developing APIs or microservices for production systems
- Solid understanding of distributed systems, data processing, and system reliability concepts
- Solid foundation in algorithms, data structures, and computational thinking
PREFERRED QUALIFICATIONS
- Familiarity with modern AI-assisted development tools and workflows (e.g., LLM-based coding assistants, agentic development environments, or similar tools)
- Exposure to machine learning workflows and the infrastructure required to support training and inference at scale
- Experience with cloud services (e.g., AWS: S3, EC2, Lambda, Glue, SageMaker, or equivalents)
- Experience with containerization (Docker/Podman, ECS)
- Experience with data lake architectures and multi-source data ingestion patterns
- Familiarity with MLOps practices, including reproducibility, model versioning, and deployment automation
- Working knowledge of statistics, econometrics, quantitative modeling, and optimization
- Familiarity with cloud cost optimization strategies and performance tuning
WHAT YOU’LL WORK ON
- Scalable cloud data platforms integrating diverse, high-volume datasets
- Foundational predictive models that adapt across markets rather than being rebuilt for each use case
- A cloud-native platform supporting data ingestion, model training, inference, and scalable deployment
- Intelligent systems for ranking, matching, scoring, and explanation of model outputs
- Robust evaluation and monitoring frameworks for model performance and reliability
- API-driven integration of AI capabilities into broader software ecosystems
WHY JOIN US?
You’ll work on challenging, high-scale systems at the intersection of data engineering, cloud infrastructure, and machine learning. This role offers the opportunity to build foundational platforms that support advanced AI capabilities while shaping architecture, tooling, and best practices in a rapidly evolving space.
INTRODUCTION
We are seeking an ML/AI Data & Cloud Engineer to design, build, and operate the cloud-native components that deliver large-scale, data-intensive AI capabilities.
This role focuses on building scalable data platforms, distributed processing systems, and model-serving infrastructure that enable end-to-end machine learning workflows in production. You will work closely with data scientists and product teams to transform advanced models into reliable, high-performance services.
The ideal candidate combines strong engineering skills with a solid grounding in quantitative methods, and is comfortable leveraging modern AI-assisted development tools to accelerate research, prototyping, and delivery.
Responsibilities
- Design and implement cloud-native data platforms that integrate structured and unstructured data into scalable data lakes or unified data systems
- Build and maintain robust ingestion pipelines using API-driven, streaming, and batch processing workflows
- Architect data processing systems for feature engineering, large-scale computation, and model training support
- Develop and operate model-serving infrastructure enabling real-time and batch inference via APIs
- Develop advanced modeling approaches combining statistical methods and machine learning (e.g., regression, gradient boosting, random forests, and ensemble/meta-learning techniques)
- Implement model validation and monitoring frameworks addressing bias, drift, robustness, and performance
- Tune cloud infrastructure for performance, reliability, and cost efficiency across compute, storage, and data transfer layers
- Build and maintain MLOps pipelines for model deployment, versioning, monitoring, and automated retraining
- Ensure platform reliability, fault tolerance, and data integrity across all stages of the data and model lifecycle
BASIC QUALIFICATIONS
- Bachelor’s degree in Computer Science, Data Engineering, Engineering, Applied Mathematics, or related field (or equivalent experience)
- 3+ years of experience in data engineering, cloud engineering, ML platform engineering, machine learning engineering, AI systems, quantitative modeling, or data-intensive platform development
- Strong programming skills in Python and experience with backend, data engineering, or experience with modern ML frameworks and data tooling
- Experience designing and operating cloud-native systems (AWS or equivalent)
- Experience building and maintaining large-scale data pipelines and working with high-volume datasets
- Experience developing APIs or microservices for production systems
- Solid understanding of distributed systems, data processing, and system reliability concepts
- Solid foundation in algorithms, data structures, and computational thinking
PREFERRED QUALIFICATIONS
- Familiarity with modern AI-assisted development tools and workflows (e.g., LLM-based coding assistants, agentic development environments, or similar tools)
- Exposure to machine learning workflows and the infrastructure required to support training and inference at scale
- Experience with cloud services (e.g., AWS: S3, EC2, Lambda, Glue, SageMaker, or equivalents)
- Experience with containerization (Docker/Podman, ECS)
- Experience with data lake architectures and multi-source data ingestion patterns
- Familiarity with MLOps practices, including reproducibility, model versioning, and deployment automation
- Working knowledge of statistics, econometrics, quantitative modeling, and optimization
- Familiarity with cloud cost optimization strategies and performance tuning
WHAT YOU’LL WORK ON
- Scalable cloud data platforms integrating diverse, high-volume datasets
- Foundational predictive models that adapt across markets rather than being rebuilt for each use case
- A cloud-native platform supporting data ingestion, model training, inference, and scalable deployment
- Intelligent systems for ranking, matching, scoring, and explanation of model outputs
- Robust evaluation and monitoring frameworks for model performance and reliability
- API-driven integration of AI capabilities into broader software ecosystems
WHY JOIN US?
You’ll work on challenging, high-scale systems at the intersection of data engineering, cloud infrastructure, and machine learning. This role offers the opportunity to build foundational platforms that support advanced AI capabilities while shaping architecture, tooling, and best practices in a rapidly evolving space.
See all OPT Cloud Data Engineer Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new OPT Cloud Data Engineer Jobs.
Get Access To All JobsTips for Finding OPT Sponsorship as a Cloud Data Engineer
Target companies with active H-1B filings
Search OFLC disclosure data to identify employers who have sponsored cloud or data engineering roles before. Past H-1B visa filings are the strongest indicator that a company understands the sponsorship process and is willing to repeat it.
Get certified on major cloud platforms
AWS, Google Cloud, and Azure certifications signal job-ready skills to hiring managers. Employers sponsoring OPT students want confidence the hire is worth the investment, and cloud credentials reduce perceived risk during the evaluation process.
Apply before your OPT start date
Hiring timelines for data engineering roles can stretch six to ten weeks. Starting your search two to three months before your OPT begins gives you enough runway to clear interviews, background checks, and offer negotiations before your work authorization activates.
Highlight pipeline and platform experience explicitly
Employers screening OPT candidates want proof of hands-on cloud work. List specific tools you have used, such as Apache Airflow, dbt, BigQuery, or Redshift, and describe the scale and outcome of each project directly on your resume.
Focus on companies with STEM OPT extension track records
Cloud Data Engineer roles almost universally qualify for the 24-month STEM OPT extension. Prioritize employers with E-Verify enrollment, which is required for STEM OPT, so you are not disqualified from extending your authorization after your initial 12 months.
Use Migrate Mate to filter for OPT-friendly roles
Not every job posting signals visa openness. Migrate Mate surfaces Cloud Data Engineer roles from employers with verified sponsorship history, so you spend less time guessing and more time applying to companies that are genuinely open to OPT candidates.
Cloud Data Engineer OPT: Frequently Asked Questions
Does Cloud Data Engineer qualify as a specialty occupation for OPT and H-1B purposes?
Yes. Cloud Data Engineer is well established as a specialty occupation. The role requires at minimum a bachelor's degree in computer science, information systems, or a related technical field, which satisfies the USCIS specialty occupation standard. This makes it one of the stronger job titles for both OPT authorization and eventual H-1B sponsorship.
Does a Cloud Data Engineer role qualify for the 24-month STEM OPT extension?
In most cases, yes. Cloud Data Engineering typically falls under CIP codes related to computer science or information technology, which are on the STEM Designated Degree Program list. Your degree field determines eligibility, not the job title. Confirm your degree's CIP code with your DSO before assuming you qualify. Your employer must also be enrolled in E-Verify for the extension to be approved.
How do I find Cloud Data Engineer jobs where the employer is open to OPT candidates?
Migrate Mate is built specifically for this. It lists Cloud Data Engineer roles from employers with documented sponsorship activity, so you can filter by OPT-friendly companies rather than applying blindly. Many standard job boards do not surface visa status preferences clearly, which wastes time on roles that will screen you out early in the process.
Can I work on cloud platforms for clients as a Cloud Data Engineer on OPT?
Yes, as long as you are employed directly by an employer who authorized your OPT. You cannot work as an independent contractor or freelancer on OPT. If your role involves client-facing cloud deployments, that is standard full-time employment activity and is fully permitted. Consulting firm arrangements are allowed only if you are a W-2 employee of the consulting firm, not a 1099 contractor.
What degree fields are most recognized for Cloud Data Engineer OPT roles?
Computer science, data science, information systems, and computer engineering are the most commonly accepted degree fields. Some employers also accept electrical engineering or applied mathematics if your coursework included significant programming or data systems components. If your degree title is less conventional, be prepared to provide a transcript showing relevant technical coursework, particularly during the H-1B petition stage with USCIS.