H-1B Visa Data Platform Engineer Jobs
Data Platform Engineer roles qualify for H-1B visa sponsorship as specialty occupations requiring at least a bachelor's degree in computer science, information systems, or a related field. Employers file the Labor Condition Application with DOL before petitioning USCIS, and the annual cap lottery runs each April for October 1 start dates.
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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.
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 Platform Engineer with the MSI team, you will design, build, and operate scalable AI data platforms that enable GenAI, Agentic AI, and Embodied AI solutions across the enterprise. You will develop reusable platform services, data pipelines, and data quality frameworks that transform fragmented enterprise and multimodal data into trusted, AI-ready datasets - combining expertise in AI data platform engineering, data quality, systems engineering, and AI data lifecycle management to accelerate AI innovation.
DESCRIPTION
Design, build, and maintain scalable AI data platforms, services, and APIs that support and enable AI model development and production.
Develop data ingestion, transformation, and publishing pipelines for structured, unstructured, and multimodal data.
Build AI-ready datasets through ground truth creation, data curation, annotation workflows, dataset versioning, and metadata management.
Develop data quality frameworks, validation pipelines, observability, and evaluation metrics to ensure trusted AI datasets.
Design and implement Retrieval-Augmented Generation (RAG) pipelines, embedding workflows, vector database integrations, and metadata services for enterprise AI applications.
Build scalable platform capabilities for managing the end-to-end AI data lifecycle, including ground truth dataset creation, dataset versioning, metadata and lineage management, automated data quality validation, governance, and secure publishing of AI-ready datasets.
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.
Drive engineering best practices for AI data architecture, platform design, automation, testing, monitoring, and operational excellence.
Evaluate emerging AI technologies and continuously improve platform capabilities that enable GenAI, agentic AI, and embodied AI solutions.
MINIMUM QUALIFICATIONS
Bachelor's or Master's degree in Computer Science, Software Engineering, Data Engineering, or a related field.
5+ 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).
Hands-on experience with AI data engineering, including ground truth dataset creation, data curation, annotation pipelines, dataset versioning, and metadata management.
Experience implementing data validation, quality frameworks, observability, and AI dataset evaluation.
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.
Strong understanding of distributed systems, APIs, microservices, and enterprise integration patterns.
Excellent communication, collaboration, and technical leadership skills.
PREFERRED QUALIFICATIONS
Experience building platforms supporting GenAI, Agentic AI, or Embodied AI applications.
Experience with multimodal datasets, knowledge graphs, AI evaluation frameworks, or vector search technologies.
Familiarity with enterprise data governance, lineage, metadata management, and AI compliance.
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 $150,400 and $277,600, 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.
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 Platform Engineer with the MSI team, you will design, build, and operate scalable AI data platforms that enable GenAI, Agentic AI, and Embodied AI solutions across the enterprise. You will develop reusable platform services, data pipelines, and data quality frameworks that transform fragmented enterprise and multimodal data into trusted, AI-ready datasets - combining expertise in AI data platform engineering, data quality, systems engineering, and AI data lifecycle management to accelerate AI innovation.
DESCRIPTION
Design, build, and maintain scalable AI data platforms, services, and APIs that support and enable AI model development and production.
Develop data ingestion, transformation, and publishing pipelines for structured, unstructured, and multimodal data.
Build AI-ready datasets through ground truth creation, data curation, annotation workflows, dataset versioning, and metadata management.
Develop data quality frameworks, validation pipelines, observability, and evaluation metrics to ensure trusted AI datasets.
Design and implement Retrieval-Augmented Generation (RAG) pipelines, embedding workflows, vector database integrations, and metadata services for enterprise AI applications.
Build scalable platform capabilities for managing the end-to-end AI data lifecycle, including ground truth dataset creation, dataset versioning, metadata and lineage management, automated data quality validation, governance, and secure publishing of AI-ready datasets.
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.
Drive engineering best practices for AI data architecture, platform design, automation, testing, monitoring, and operational excellence.
Evaluate emerging AI technologies and continuously improve platform capabilities that enable GenAI, agentic AI, and embodied AI solutions.
MINIMUM QUALIFICATIONS
Bachelor's or Master's degree in Computer Science, Software Engineering, Data Engineering, or a related field.
5+ 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).
Hands-on experience with AI data engineering, including ground truth dataset creation, data curation, annotation pipelines, dataset versioning, and metadata management.
Experience implementing data validation, quality frameworks, observability, and AI dataset evaluation.
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.
Strong understanding of distributed systems, APIs, microservices, and enterprise integration patterns.
Excellent communication, collaboration, and technical leadership skills.
PREFERRED QUALIFICATIONS
Experience building platforms supporting GenAI, Agentic AI, or Embodied AI applications.
Experience with multimodal datasets, knowledge graphs, AI evaluation frameworks, or vector search technologies.
Familiarity with enterprise data governance, lineage, metadata management, and AI compliance.
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 $150,400 and $277,600, 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.
See all 1,223+ H-1B Visa Data Platform Engineer Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new H-1B Visa Data Platform Engineer Jobs.
Get Access To All JobsTips for Finding H-1B Visa Sponsorship as a Data Platform Engineer
Align your degree to platform engineering
USCIS evaluates whether your specific degree field supports a Data Platform Engineer role. A degree in computer science, data engineering, or information systems maps cleanly. A business or unrelated degree requires a credential evaluation showing equivalency to a qualifying technical field.
Filter employers by LCA filing history
Use Migrate Mate to search Data Platform Engineer roles filtered by employers with active H-1B LCA filings. This removes guesswork about whether a company sponsors, letting you focus outreach on employers with a verified track record of filing for this occupation.
Check prevailing wage before your offer
Run your target job title and location through the OFLC Wage Search before salary negotiations. Your offered wage must meet the DOL prevailing wage for your SOC code and metro area, so knowing the floor in advance prevents offers that can't clear LCA certification.
Verify the role's specialty occupation standing
Not every data or infrastructure role qualifies as a specialty occupation. Review the O*NET profile for Data Platform Engineer or closely matched titles to confirm the occupation requires a specific bachelor's degree, which is the foundation USCIS uses to approve H-1B petitions.
Request premium processing in your offer negotiation
Standard I-129 adjudication can run several months, which creates uncertainty around your start date. Asking your employer to file with USCIS premium processing reduces the decision window to 15 business days and removes timeline ambiguity during onboarding.
Understand cap-exempt employer pathways
Universities, nonprofit research organizations, and government research entities are exempt from the H-1B annual cap and lottery. Data Platform Engineer roles at these institutions can be filed and approved at any point in the year, bypassing the April registration window entirely.
H-1B Visa Data Platform Engineer: Frequently Asked Questions
Does a Data Platform Engineer role qualify as an H-1B specialty occupation?
Yes. Data Platform Engineer positions require theoretical and practical application of highly specialized knowledge, typically a bachelor's degree or higher in computer science, data engineering, or information systems. USCIS evaluates the degree requirement against the specific duties in the role, so the job description must reflect that a generalist degree isn't sufficient for the position.
Which employers consistently sponsor H-1B visas for Data Platform Engineers?
Large technology firms, cloud infrastructure companies, financial services institutions, and healthcare data organizations regularly file H-1B LCAs for data platform roles. Migrate Mate surfaces employers with verified LCA filing history for this occupation, so you can identify which companies actively sponsor rather than relying on job descriptions that may or may not include sponsorship.
How does the H-1B cap lottery affect my timeline as a Data Platform Engineer candidate?
USCIS opens H-1B registration each March for the following fiscal year, with an October 1 start date for selected petitions. If you're on OPT or another status, your employer must submit your registration before the window closes. Cap-exempt employers, including universities and qualifying nonprofits, can file outside this cycle, which is worth targeting if your timeline is tight.
Can my employer file my H-1B petition before I finish my OPT period?
Yes, and timing this correctly matters. If your employer files your H-1B petition before your OPT expires and you're selected in the lottery, cap-gap protection extends your work authorization through September 30 or until your H-1B is approved, whichever comes first. USCIS automatically grants this extension, but your employer's filing date is what triggers it.
What documentation do I need from my employer for an H-1B petition as a Data Platform Engineer?
Your employer needs a certified Labor Condition Application from DOL confirming the offered wage meets prevailing wage requirements for your SOC code and location. The I-129 petition must also include a detailed job duties letter establishing specialty occupation, your academic credentials, and any relevant certifications like AWS or Google Cloud if the role specifically requires them.