AI Data Engineer Jobs in USA with Visa Sponsorship
AI Data Engineers are in high demand from H-1B visa and O-1 visa sponsors across tech, finance, and healthcare. The role qualifies as a specialty occupation, and employers regularly file LCAs under computer science and data engineering job codes. For detailed occupation requirements, see the O*NET profile.
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Get Access To All JobsAbout Monster Energy:
Forget about blending in. That's not our style. We're the risk takers, the trailblazers, the gamechangers. We're not perfect and we don't pretend to be. We're raw, unfiltered, and a bit unconventional. Our drive is just like our athletes, unrivaled. The power is in your hands to define what success looks like and where you want to take your career. It's not just about what we do, but about who we become along the way. We are much more than a brand here. We are a way of life, a mindset. Join us.
A Day in the Life:
In the position of AI Data Engineer, you will engineer the data that Monster's artificial intelligence (AI) agents and copilots depend on, owning AI data readiness from source to answer. Assess and remediate data for specific AI use cases; build the retrieval pipelines, embeddings, and search indexes that ground Microsoft Copilot, Copilot Studio, Agentforce, and custom OpenAI and Claude-based agents; and maintain the semantic layer that links related records across systems (for example, a Salesforce account to its SAP financials and Power BI reporting). Working in Microsoft Fabric, Azure, and Databricks with the AI Architect, Sr. AI Solution Engineer, and AI Engineer, make enterprise data AI-ready, measurable, and trustworthy, so every AI answer can be traced to its source.
The Impact You'll Make:
-
Retrieval (RAG) Data Pipelines — Build and operate ingestion, parsing, chunking, enrichment, embedding, and vector / hybrid indexing pipelines (e.g., Azure AI Search, Databricks Vector Search) that ground Copilot, Copilot Studio, Agentforce, and custom agents; enforce permission-aware retrieval that respects source-system security.
-
Semantic Layer & Grounding Context — Design and maintain Power BI / Fabric semantic models, business glossary, and entity relationships (e.g., customer, product, and distributor across Salesforce, SAP, and BI) that give AI agents and Fabric data agents a shared, accurate understanding of the business.
-
AI Data Readiness Assessment & Remediation — Assess source data for each AI use case (coverage, quality, freshness, sensitivity, and bias); score AI readiness; and drive remediation with source owners before AI build begins.
-
AI Data Access via MCP & APIs — Expose curated, zero-copy data in Microsoft Fabric / OneLake and Databricks to AI agents through governed interfaces (e.g., MCP servers, SQL endpoints, APIs) with least-privilege access.
-
Retrieval Quality, Observability & Feedback — Monitor index freshness, data drift, and retrieval quality (relevance, groundedness, citation accuracy); feed agent logs and user feedback back into data fixes; publish AI-readiness key performance indicators (KPIs) in Power BI.
-
Metadata, Lineage & AI Data Governance — Register AI data assets in Microsoft Purview or Collibra; apply sensitivity labels; maintain lineage from source to index, agent, and answer; partner with Legal, Privacy, and Security on personal data, retention, EMEA data residency, and AI regulatory requirements.
-
Evaluation & Ground-Truth Datasets — Curate golden question-and-answer sets, labeled ground truth, and test data that measure AI accuracy; prepare data for fine-tuning or model customization when needed, including LLM-generated synthetic data with appropriate controls.
-
LLM-Assisted Data Engineering & Standards — Use OpenAI and Claude models and AI coding tools (e.g., Cursor) to automate metadata enrichment, entity matching, documentation, and pipeline code; contribute AI data standards, templates, and playbooks.
Who You Are:
-
Prefer a Bachelor's Degree in the field of Computer Science, Artificial Intelligence, Information Systems, Statistics, or related.
-
3-5 years of experience in data engineering (pipelines, data modeling, SQL, Spark), including work on Microsoft Azure / Fabric or Databricks.
-
1-3 years of experience in building data pipelines specifically for AI or generative AI use cases (RAG, embeddings, vector search, semantic models, evaluation datasets).
-
Computer Skills Desired: Languages / query: SQL (advanced), Python (data tooling, PySpark), Spark (intermediate).
-
Platforms: Microsoft Fabric / OneLake (Data Factory, Lakehouse, Warehouse, data agents), Azure data services, Databricks, or equivalent.
-
AI data tooling: Azure AI Search and vector / hybrid search, OpenAI embedding models via Azure OpenAI, Azure AI Document Intelligence, LangChain or similar for ingestion, MCP servers for data access.
-
Models: OpenAI GPT-5.6 / GPT-6 and Anthropic Claude Sonnet 5.5 / Haiku 5.5 for enrichment, classification, and evaluation.
-
Metadata / catalog: Microsoft Purview, Collibra (or similar); Git for versioning.
-
Modeling / orchestration: dbt, Azure Data Factory / Fabric pipelines, or Airflow.
-
Dashboards for AI-readiness KPIs: Power BI.
-
AI-assisted development: Cursor, Claude Code, or GitHub Copilot.
-
Preferred Certifications: Microsoft Certified: Fabric Data Engineer Associate (DP-700), which replaces the retired Azure Data Engineer Associate (DP-203).
-
Databricks Certified Data Engineer Associate or Generative AI Engineer Associate.
-
Microsoft AI-103: Developing AI Apps and Agents on Azure (desirable).
-
Collibra or Microsoft Purview practitioner training / certification.
-
Additional Knowledge or Skills to be Successful in this role: Strong collaboration with the AI Architect, Sr. AI Solution Engineer, AI Engineer, data engineering, governance, and security teams; ability to turn business concepts into semantic models, entity relationships, and retrieval-ready data; treats retrieval quality and data trust as measurable outcomes.
Monster Energy uses AI-powered tools, including Greenhouse's Talent Matching feature, to help evaluate applications for this role. To learn more see Monster's privacy policy here Privacy Policy or contact Monster at privacy@monsterenergy.com.
For United States applicants:
Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities
This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights notice from the Department of Labor.
Read our privacy policy here: Privacy Policy
About Monster Energy:
Forget about blending in. That's not our style. We're the risk takers, the trailblazers, the gamechangers. We're not perfect and we don't pretend to be. We're raw, unfiltered, and a bit unconventional. Our drive is just like our athletes, unrivaled. The power is in your hands to define what success looks like and where you want to take your career. It's not just about what we do, but about who we become along the way. We are much more than a brand here. We are a way of life, a mindset. Join us.
A Day in the Life:
In the position of AI Data Engineer, you will engineer the data that Monster's artificial intelligence (AI) agents and copilots depend on, owning AI data readiness from source to answer. Assess and remediate data for specific AI use cases; build the retrieval pipelines, embeddings, and search indexes that ground Microsoft Copilot, Copilot Studio, Agentforce, and custom OpenAI and Claude-based agents; and maintain the semantic layer that links related records across systems (for example, a Salesforce account to its SAP financials and Power BI reporting). Working in Microsoft Fabric, Azure, and Databricks with the AI Architect, Sr. AI Solution Engineer, and AI Engineer, make enterprise data AI-ready, measurable, and trustworthy, so every AI answer can be traced to its source.
The Impact You'll Make:
-
Retrieval (RAG) Data Pipelines — Build and operate ingestion, parsing, chunking, enrichment, embedding, and vector / hybrid indexing pipelines (e.g., Azure AI Search, Databricks Vector Search) that ground Copilot, Copilot Studio, Agentforce, and custom agents; enforce permission-aware retrieval that respects source-system security.
-
Semantic Layer & Grounding Context — Design and maintain Power BI / Fabric semantic models, business glossary, and entity relationships (e.g., customer, product, and distributor across Salesforce, SAP, and BI) that give AI agents and Fabric data agents a shared, accurate understanding of the business.
-
AI Data Readiness Assessment & Remediation — Assess source data for each AI use case (coverage, quality, freshness, sensitivity, and bias); score AI readiness; and drive remediation with source owners before AI build begins.
-
AI Data Access via MCP & APIs — Expose curated, zero-copy data in Microsoft Fabric / OneLake and Databricks to AI agents through governed interfaces (e.g., MCP servers, SQL endpoints, APIs) with least-privilege access.
-
Retrieval Quality, Observability & Feedback — Monitor index freshness, data drift, and retrieval quality (relevance, groundedness, citation accuracy); feed agent logs and user feedback back into data fixes; publish AI-readiness key performance indicators (KPIs) in Power BI.
-
Metadata, Lineage & AI Data Governance — Register AI data assets in Microsoft Purview or Collibra; apply sensitivity labels; maintain lineage from source to index, agent, and answer; partner with Legal, Privacy, and Security on personal data, retention, EMEA data residency, and AI regulatory requirements.
-
Evaluation & Ground-Truth Datasets — Curate golden question-and-answer sets, labeled ground truth, and test data that measure AI accuracy; prepare data for fine-tuning or model customization when needed, including LLM-generated synthetic data with appropriate controls.
-
LLM-Assisted Data Engineering & Standards — Use OpenAI and Claude models and AI coding tools (e.g., Cursor) to automate metadata enrichment, entity matching, documentation, and pipeline code; contribute AI data standards, templates, and playbooks.
Who You Are:
-
Prefer a Bachelor's Degree in the field of Computer Science, Artificial Intelligence, Information Systems, Statistics, or related.
-
3-5 years of experience in data engineering (pipelines, data modeling, SQL, Spark), including work on Microsoft Azure / Fabric or Databricks.
-
1-3 years of experience in building data pipelines specifically for AI or generative AI use cases (RAG, embeddings, vector search, semantic models, evaluation datasets).
-
Computer Skills Desired: Languages / query: SQL (advanced), Python (data tooling, PySpark), Spark (intermediate).
-
Platforms: Microsoft Fabric / OneLake (Data Factory, Lakehouse, Warehouse, data agents), Azure data services, Databricks, or equivalent.
-
AI data tooling: Azure AI Search and vector / hybrid search, OpenAI embedding models via Azure OpenAI, Azure AI Document Intelligence, LangChain or similar for ingestion, MCP servers for data access.
-
Models: OpenAI GPT-5.6 / GPT-6 and Anthropic Claude Sonnet 5.5 / Haiku 5.5 for enrichment, classification, and evaluation.
-
Metadata / catalog: Microsoft Purview, Collibra (or similar); Git for versioning.
-
Modeling / orchestration: dbt, Azure Data Factory / Fabric pipelines, or Airflow.
-
Dashboards for AI-readiness KPIs: Power BI.
-
AI-assisted development: Cursor, Claude Code, or GitHub Copilot.
-
Preferred Certifications: Microsoft Certified: Fabric Data Engineer Associate (DP-700), which replaces the retired Azure Data Engineer Associate (DP-203).
-
Databricks Certified Data Engineer Associate or Generative AI Engineer Associate.
-
Microsoft AI-103: Developing AI Apps and Agents on Azure (desirable).
-
Collibra or Microsoft Purview practitioner training / certification.
-
Additional Knowledge or Skills to be Successful in this role: Strong collaboration with the AI Architect, Sr. AI Solution Engineer, AI Engineer, data engineering, governance, and security teams; ability to turn business concepts into semantic models, entity relationships, and retrieval-ready data; treats retrieval quality and data trust as measurable outcomes.
Monster Energy uses AI-powered tools, including Greenhouse's Talent Matching feature, to help evaluate applications for this role. To learn more see Monster's privacy policy here Privacy Policy or contact Monster at privacy@monsterenergy.com.
For United States applicants:
Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities
This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights notice from the Department of Labor.
Read our privacy policy here: Privacy Policy
See all 15,207+ AI Data Engineer Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new AI Data Engineer roles.
Get Access To All JobsTips for Finding Visa Sponsorship as an AI Data Engineer
Target companies with active LCA filings
Employers who have filed Labor Condition Applications for data engineering or machine learning roles in the past 12 months are already set up to sponsor. These companies have legal counsel, established workflows, and budgets allocated for visa costs.
Emphasize your ML pipeline and infrastructure work
USCIS approves AI Data Engineer petitions more cleanly when job duties are tied to a specific technical field. Highlight work on model training pipelines, feature stores, or distributed data systems, not general data tasks.
A computer science or data engineering degree strengthens your case
H-1B specialty occupation requires a degree in a directly related field. CS, software engineering, statistics, and applied mathematics are strong fits. A general business or unrelated degree may require additional documentation or a credentials evaluation.
Cap-exempt employers offer a faster path in
Universities, nonprofit research institutions, and government-affiliated organizations are exempt from the H-1B lottery. AI and data roles at these employers can be filed any time of year, with USCIS adjudicating without a cap slot.
O-1A is a realistic alternative if your profile is strong
AI Data Engineers with published research, open-source contributions with wide adoption, or recognition from the field may qualify for the O-1A extraordinary ability visa. It has no lottery and no annual cap, making it faster to obtain.
Use Migrate Mate to find roles already set up to sponsor
Not every job posting discloses visa sponsorship upfront. Migrate Mate filters for employers actively sponsoring AI and data engineering roles, so you apply to companies already prepared to file, not ones figuring it out for the first time.
Frequently Asked Questions
Does the AI Data Engineer role qualify for H-1B sponsorship?
Yes. AI Data Engineer is a specialty occupation under USCIS guidelines because it requires a bachelor's degree or higher in a specific technical field such as computer science, data engineering, or statistics. Employers regularly file H-1B visa petitions for this title, and USCIS approves them at high rates when the job duties and degree field align clearly.
What degree do I need for an employer to sponsor my H-1B as an AI Data Engineer?
A bachelor's degree in computer science, data science, statistics, applied mathematics, or a closely related field is the standard. A general business or humanities degree typically won't support the petition without supplemental evidence. If your degree is in a tangentially related field, employers may need to file additional documentation demonstrating the connection between your coursework and the role.
Can I get sponsored as an AI Data Engineer if I didn't win the H-1B lottery?
There are a few paths. Cap-exempt employers, including universities and qualifying nonprofits, can file H-1B petitions year-round without lottery exposure. Alternatively, the O-1A visa has no cap and no lottery, and is achievable for AI Data Engineers with strong publication records, recognized open-source projects, or other evidence of extraordinary ability in the field.
How common is visa sponsorship for AI Data Engineer roles specifically?
AI and machine learning data roles are among the most actively sponsored positions in the U.S. tech sector. DOL LCA disclosure data consistently shows high filing volumes for data engineering and ML infrastructure titles at large technology companies and well-funded startups. Browse Migrate Mate to see which employers are currently hiring AI Data Engineers with sponsorship.
Does hands-on AI experience matter more than a formal degree for sponsorship purposes?
For H-1B purposes, the formal degree requirement is a legal threshold, not just a preference. However, USCIS also allows three years of specialized experience to substitute for one year of a four-year degree, so candidates with significant hands-on AI pipeline work and some formal education can still qualify. Document your experience clearly in any petition supporting the specialty occupation argument.
What is the prevailing wage requirement for sponsored AI Data 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.