Data Infrastructure Engineer Jobs in USA with Visa Sponsorship
Data Infrastructure Engineers are strong H-1B visa sponsorship candidates. The role qualifies as a specialty occupation requiring a bachelor's degree in computer science, information systems, or a related field, and employers in cloud, fintech, and enterprise tech sponsor it regularly. For detailed occupation requirements, see the O*NET profile.
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Get Access To All JobsINTRODUCTION
Imagine what you could do here. At Apple, we believe new insights have a way of becoming excellent products, services, and customer experiences very quickly. Bring passion and dedication to your job and there’s no telling what you could accomplish.
The people here at Apple don’t just build products - they build the kind of wonder that’s revolutionized entire industries. It’s the diversity of those people and their ideas that inspires the innovation that runs through everything we do, from amazing technology to industry-leading environmental efforts. Join Apple, and help us leave the world better than we found it.
DESCRIPTION
Manufacturing Systems and Infrastructure (MSI) team is an engineering organization under the Product Operations org. MSI is responsible for the design, development, and maintenance of systems tools, services, and applications required to efficiently run manufacturing operations at scale across global factory sites.
As an AI Data & Infrastructure Engineer with the MSI team, you will own the end to end design, build, and operation of scalable AI data systems that power enterprise GenAI and Agentic AI capabilities. Your work spans core platform services, data pipeline development and infrastructure provisioning, enabling manufacturing workflow automation through Agents and Agent skills.
Responsibilities
- Create robust, scalable architectures for systems that handle data orchestration for AI features
- Design, build, and maintain scalable AI data platforms, services, and APIs that support and enable Agentic AI workflow development & automation.
- Develop data ingestion, transformation, and publishing pipelines for structured, unstructured, and multimodal data.
- Design and implement Retrieval-Augmented Generation (RAG) pipelines, embedding workflows, vector database integrations, and metadata services for enterprise AI applications.
- Be able to quickly build an idea so you and the team can work with hands-on products. Then iterate on the best of those prototypes.
- Build and integrate tools that help make complex AI systems observable, understandable and debuggable
- Strong understanding of distributed systems, parallel computing, and performance optimization
- Collaborate with AI/ML engineers, software engineers, product teams, and domain experts to define AI data requirements and deliver production-ready data solutions.
- Optimize platform scalability, reliability, performance, security, and cost across cloud-native environments and Agentic systems.
- Clearly communicate complex technical problems and collaborate with partners to develop solutions
MINIMUM QUALIFICATIONS
- Bachelor's or Master's degree in Computer Science, Software Engineering, Data Engineering, or a related field.
- 8+ Experience designing and building scalable data platforms and distributed systems.
- Strong programming skills in Python and SQL, with proficiency in Java or Scala preferred.
- Experience with Airflow, Kubeflow, or MLflow to build and orchestrate scalable AI data pipelines.
- Experience building scalable batch and streaming data pipelines using Spark (PySpark), Kafka, Airflow, and Ray, with proficiency in Pandas and modern data lake/lakehouse architectures (e.g., Iceberg, Delta Lake).
- Experience using modern development tools, including AI-assisted coding tools, while applying sound engineering judgment to review, validate, and improve generated code.
- Knowledge of RAG architectures, embedding generation, vector databases, and AI data preparation for LLMs and agentic AI.
- Experience with cloud platforms (AWS, Azure, or GCP), Kubernetes, Docker, CI/CD, and Infrastructure as Code.
- Demonstrated ability to understand complex user workflows, translate them into practical technical solutions, and collaborate across teams to deliver measurable outcomes.
- Excellent communication and collaboration skills, with the ability to translate technical concepts into clear, business focused insights.
PREFERRED QUALIFICATIONS
- Experience with or a strong understanding of Generative AI, LLMs, Agentic Systems, or RAG (Retrieval-Augmented Generation) workflows.
- Experience integrating LLMs into existing systems
- Experience with API design, both for other engineers to use, but also for AI systems.
- Experience working with manufacturing, operational, IoT, or industrial data platforms.
- Demonstrated ability to lead technical initiatives and mentor engineers.
PAY & BENEFITS
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $184,700 and $324,800, and your base pay will depend on your skills, qualifications, experience, and location.
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.
INTRODUCTION
Imagine what you could do here. At Apple, we believe new insights have a way of becoming excellent products, services, and customer experiences very quickly. Bring passion and dedication to your job and there’s no telling what you could accomplish.
The people here at Apple don’t just build products - they build the kind of wonder that’s revolutionized entire industries. It’s the diversity of those people and their ideas that inspires the innovation that runs through everything we do, from amazing technology to industry-leading environmental efforts. Join Apple, and help us leave the world better than we found it.
DESCRIPTION
Manufacturing Systems and Infrastructure (MSI) team is an engineering organization under the Product Operations org. MSI is responsible for the design, development, and maintenance of systems tools, services, and applications required to efficiently run manufacturing operations at scale across global factory sites.
As an AI Data & Infrastructure Engineer with the MSI team, you will own the end to end design, build, and operation of scalable AI data systems that power enterprise GenAI and Agentic AI capabilities. Your work spans core platform services, data pipeline development and infrastructure provisioning, enabling manufacturing workflow automation through Agents and Agent skills.
Responsibilities
- Create robust, scalable architectures for systems that handle data orchestration for AI features
- Design, build, and maintain scalable AI data platforms, services, and APIs that support and enable Agentic AI workflow development & automation.
- Develop data ingestion, transformation, and publishing pipelines for structured, unstructured, and multimodal data.
- Design and implement Retrieval-Augmented Generation (RAG) pipelines, embedding workflows, vector database integrations, and metadata services for enterprise AI applications.
- Be able to quickly build an idea so you and the team can work with hands-on products. Then iterate on the best of those prototypes.
- Build and integrate tools that help make complex AI systems observable, understandable and debuggable
- Strong understanding of distributed systems, parallel computing, and performance optimization
- Collaborate with AI/ML engineers, software engineers, product teams, and domain experts to define AI data requirements and deliver production-ready data solutions.
- Optimize platform scalability, reliability, performance, security, and cost across cloud-native environments and Agentic systems.
- Clearly communicate complex technical problems and collaborate with partners to develop solutions
MINIMUM QUALIFICATIONS
- Bachelor's or Master's degree in Computer Science, Software Engineering, Data Engineering, or a related field.
- 8+ Experience designing and building scalable data platforms and distributed systems.
- Strong programming skills in Python and SQL, with proficiency in Java or Scala preferred.
- Experience with Airflow, Kubeflow, or MLflow to build and orchestrate scalable AI data pipelines.
- Experience building scalable batch and streaming data pipelines using Spark (PySpark), Kafka, Airflow, and Ray, with proficiency in Pandas and modern data lake/lakehouse architectures (e.g., Iceberg, Delta Lake).
- Experience using modern development tools, including AI-assisted coding tools, while applying sound engineering judgment to review, validate, and improve generated code.
- Knowledge of RAG architectures, embedding generation, vector databases, and AI data preparation for LLMs and agentic AI.
- Experience with cloud platforms (AWS, Azure, or GCP), Kubernetes, Docker, CI/CD, and Infrastructure as Code.
- Demonstrated ability to understand complex user workflows, translate them into practical technical solutions, and collaborate across teams to deliver measurable outcomes.
- Excellent communication and collaboration skills, with the ability to translate technical concepts into clear, business focused insights.
PREFERRED QUALIFICATIONS
- Experience with or a strong understanding of Generative AI, LLMs, Agentic Systems, or RAG (Retrieval-Augmented Generation) workflows.
- Experience integrating LLMs into existing systems
- Experience with API design, both for other engineers to use, but also for AI systems.
- Experience working with manufacturing, operational, IoT, or industrial data platforms.
- Demonstrated ability to lead technical initiatives and mentor engineers.
PAY & BENEFITS
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $184,700 and $324,800, and your base pay will depend on your skills, qualifications, experience, and location.
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.
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Get Access To All JobsTips for Finding Visa Sponsorship as a Data Infrastructure Engineer
Target employers with established H-1B filing histories
Large tech companies and cloud providers file H-1B petitions for data infrastructure roles consistently. Focusing on employers with multi-year sponsorship track records significantly reduces the risk of an offer falling through mid-process.
Align your degree field to the job description
USCIS requires a direct relationship between your degree and the role. A computer science or information systems degree maps cleanly. If your degree is in a related but adjacent field, gather documentation showing the coursework connection early.
Understand where you fit in the H-1B lottery
If you hold a U.S. master's degree in a qualifying field, you're entered into a separate pool with better selection odds. Cap-exempt employers, including certain nonprofits and universities, let you bypass the lottery entirely.
Time your job search around the H-1B filing window
H-1B petitions are filed in April for an October 1 start date. Starting your search in late fall or winter gives employers enough runway to sponsor you before the registration window opens in March.
Be specific about your technical scope on your resume
USCIS scrutinizes specialty occupation claims. Clearly listing infrastructure tools, cloud platforms, and data pipeline technologies you've worked with strengthens the employer's petition and reduces the likelihood of a Request for Evidence.
Browse open roles on Migrate Mate before approaching employers
Not every company that hires data infrastructure engineers will sponsor visas. Migrate Mate filters for verified sponsoring employers, so you spend time applying to roles where sponsorship is already confirmed rather than guessing.
Frequently Asked Questions
Does a Data Infrastructure Engineer role qualify for H-1B sponsorship?
Yes. Data Infrastructure Engineer is a specialty occupation under USCIS standards because it typically requires a bachelor's degree or higher in computer science, information systems, or a closely related field. Employers must demonstrate the degree requirement is standard for the position, which is straightforward for most infrastructure engineering roles at technology companies.
What degree do I need to get sponsored as a Data Infrastructure Engineer?
A bachelor's degree in computer science, information systems, electrical engineering, or a related technical field is the standard baseline. Some employers accept degrees in mathematics or physics with sufficient relevant coursework. USCIS also recognizes three years of specialized work experience as a substitute for each year of missing formal education, though a direct degree match makes the petition significantly cleaner.
Do Data Infrastructure Engineer roles have good H-1B approval rates?
Software and infrastructure engineering roles consistently see approval rates above 85% in recent USCIS data, making them among the more favorable categories for sponsorship. Denials in this space typically stem from weak specialty occupation arguments in the petition, not the role itself. Working with an employer who uses experienced immigration counsel matters more than the job title alone.
Can I get sponsored for a Data Infrastructure role if I'm currently on OPT or STEM OPT?
Yes, and STEM OPT is a common bridge for this role. Data infrastructure positions generally qualify for the 24-month STEM OPT extension because they fall under CIP codes in computer science and information technology. Your employer must be E-Verify enrolled for STEM OPT, and you'll need to transition to H-1B visa status before your OPT authorization expires.
How do I find Data Infrastructure Engineer jobs that actually offer visa sponsorship?
Most job postings don't clearly indicate whether sponsorship is available, which wastes significant time during an already stressful search. Migrate Mate curates and verifies sponsoring employers specifically for visa-dependent candidates, so every role on the platform is one where sponsorship has been confirmed. Filtering by role type on Migrate Mate is the most direct way to find verified opportunities.
What is the prevailing wage requirement for sponsored Data Infrastructure Engineer jobs?
U.S. employers sponsoring a visa must pay at least the prevailing wage, which is what workers in the same role, area, and experience level typically earn. The Department of Labor sets this rate to make sure companies aren't hiring foreign workers simply because they'd accept lower pay than a U.S. worker. It varies by job title, location, and experience. You can look up current prevailing wage rates for any occupation and location using the OFLC Wage Search page.