H-1B1 Chile Visa Data Engineer Jobs
Data Engineer jobs with H-1B1 Chile visa sponsorship are open to Chilean nationals under the U.S.-Chile Free Trade Agreement. No lottery, no USCIS petition filing, and the 1,400-visa annual cap rarely fills. Employers with active data infrastructure needs file a Labor Condition Application and you apply directly at the consulate.
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The Lead IT Data Engineer owns and drives the organization's data strategy, setting the technical direction for data engineering, enterprise data modeling, and agentic AI adoption. This role is responsible for platform architecture, tool selection, cloud strategy, budget and capacity planning, and the design of enterprise AI agent systems — all while establishing governance frameworks for responsible data and AI practices and mentoring the engineering team.
Essential Duties and Responsibilities
- Own and drive the overall data strategy, including the multi-quarter technical roadmap, platform architecture, and data engineering standards.
- Architect end-to-end data solutions across cloud platforms (AWS, GCP, or Azure), setting standards for orchestration (Airflow, Dagster), transformation (dbt), streaming (Kafka, Flink), and storage (Delta Lake, Iceberg, Snowflake, BigQuery).
- Own the enterprise data modeling strategy — crafting scalable models using dimensional, multi-dimensional, and advanced normalization techniques, with enterprise-wide documentation and metadata governance.
- Define API design standards and data contracts to ensure reliable, well-governed interfaces between data producers and consumers.
- Establish enterprise-level data governance, security, and compliance frameworks across all data and AI systems, including access controls, cataloging, and lineage.
- Define and enforce CI/CD standards for data pipelines, containerized architectures (Docker, K8s), and infrastructure as code (Terraform).
- Drive data observability practices and platform reliability at enterprise scale.
- Drive build-vs-buy evaluations for data and AI tools, considering TCO, vendor lock-in, scalability, and organizational fit; manage vendor relationships.
- Own or co-own infrastructure budget and capacity planning for data platform resources; optimize cloud costs at the organizational level.
- Define and drive the organization's agentic AI strategy, architecting enterprise-scale multi-agent systems, autonomous data pipelines, and RAG/knowledge graph platforms.
- Establish AI governance frameworks, including ethics policies, bias detection, safety guardrails, security standards (prompt injection, data exfiltration, PII), and compliance with emerging regulations (e.g., EU AI Act).
- Establish LLMOps practices at scale — model deployment, prompt versioning, A/B testing, performance monitoring, drift detection, and cost optimization.
- Design human-in-the-loop escalation paths for critical AI-driven decisions, ensuring appropriate oversight.
- Lead AI platform evaluation and integration, including TCO analysis, data residency, and SLA requirements for agentic frameworks.
- Set software engineering best practices — code review standards, design patterns, technical debt management, and documentation.
- Advocate for and lead adoption of data mesh and data-as-a-product principles.
- Mentor the engineering team on data engineering, data modeling, and AI best practices.
- Perform other assigned job-related duties that align with our organization's vision, mission, and values and fall within your scope of practice.
Qualifications
Education: Bachelor's Degree or relevant experience.
Preferred Certification(s): AWS Solutions Architect Professional, Google Professional Data Engineer, Azure Solutions Architect Expert, Databricks Certified Data Engineer Professional, or equivalent.
Experience: 5+ years of relevant and practical experience.
Special Skills
- Expert proficiency in Python and SQL for data engineering at scale.
- Expertise in modern data platforms (Databricks, Snowflake, BigQuery), lakehouse architectures (Delta Lake, Iceberg), and streaming (Kafka, Flink, Pub/Sub).
- Deep expertise in at least one major cloud platform with cross-cloud awareness.
- Mastery of orchestration, transformation (dbt), containerization (Docker, K8s), and IaC (Terraform).
- Advanced enterprise data modeling, warehousing, data contracts, and API design.
- Expertise in CI/CD, data observability, governance, data mesh, and platform reliability.
- Experience in technical roadmap ownership, build-vs-buy evaluation, and budget/capacity planning.
- Expert-level knowledge of agentic AI architectures, LLMOps, RAG, knowledge graphs, and AI governance/safety/security.
Soft Skills
- Leadership: Owning and driving data and AI strategy across the organization.
- Strategic Vision: Translating business objectives into actionable technical roadmaps.
- Stakeholder Management: Building relationships with partners and executive leadership.
- Communication: Presenting complex data and AI concepts to board-level audiences.
- Mentorship: Developing the data engineering team's data and AI competencies.
- Decision-Making: Making high-impact choices on architecture, platforms, and investments.
- Change Management: Guiding the organization through data and AI transformations.
- Innovation & Thought Leadership: Driving industry best practices in data engineering, modeling, and agentic AI.
- Negotiation: Balancing technical requirements with business needs and resource constraints.
Relocation Assistance Eligible:
No
Work Shift:
1ST SHIFT (United States of America)
Certain roles at Tyson require background checks. If you are offered a position that requires a background check you will be provided additional documentation to complete once an offer has been extended.
Hourly Applicants ONLY - You must complete the task after submitting your application to provide additional information to be considered for employment.
Tyson is an Equal Opportunity Employer. All qualified applicants will be considered without regard to race, national origin, color, religion, age, genetics, sex, sexual orientation, gender identity, disability or veteran status.
We provide our team members and their families with paid time off; 401(k) plans; affordable health, life, dental, vision and prescription drug benefits; and more.
If you would like to learn more about your data privacy rights and how you may use that information, please read our Job Applicant Privacy Notice here.
Unsolicited Assistance: Tyson Foods and its subsidiaries do not accept unsolicited support from external recruitment vendors for open positions within the United States. Any resumes or candidate profiles submitted by recruitment vendors or headhunters to any employee or applicant tracking system at Tyson Foods or its subsidiaries, without a valid written request and search agreement approved by HR, will be considered the property of Tyson Foods. No fees will be paid if the candidate is hired due to an unsolicited referral.
Job Details:
The Lead IT Data Engineer owns and drives the organization's data strategy, setting the technical direction for data engineering, enterprise data modeling, and agentic AI adoption. This role is responsible for platform architecture, tool selection, cloud strategy, budget and capacity planning, and the design of enterprise AI agent systems — all while establishing governance frameworks for responsible data and AI practices and mentoring the engineering team.
Essential Duties and Responsibilities
- Own and drive the overall data strategy, including the multi-quarter technical roadmap, platform architecture, and data engineering standards.
- Architect end-to-end data solutions across cloud platforms (AWS, GCP, or Azure), setting standards for orchestration (Airflow, Dagster), transformation (dbt), streaming (Kafka, Flink), and storage (Delta Lake, Iceberg, Snowflake, BigQuery).
- Own the enterprise data modeling strategy — crafting scalable models using dimensional, multi-dimensional, and advanced normalization techniques, with enterprise-wide documentation and metadata governance.
- Define API design standards and data contracts to ensure reliable, well-governed interfaces between data producers and consumers.
- Establish enterprise-level data governance, security, and compliance frameworks across all data and AI systems, including access controls, cataloging, and lineage.
- Define and enforce CI/CD standards for data pipelines, containerized architectures (Docker, K8s), and infrastructure as code (Terraform).
- Drive data observability practices and platform reliability at enterprise scale.
- Drive build-vs-buy evaluations for data and AI tools, considering TCO, vendor lock-in, scalability, and organizational fit; manage vendor relationships.
- Own or co-own infrastructure budget and capacity planning for data platform resources; optimize cloud costs at the organizational level.
- Define and drive the organization's agentic AI strategy, architecting enterprise-scale multi-agent systems, autonomous data pipelines, and RAG/knowledge graph platforms.
- Establish AI governance frameworks, including ethics policies, bias detection, safety guardrails, security standards (prompt injection, data exfiltration, PII), and compliance with emerging regulations (e.g., EU AI Act).
- Establish LLMOps practices at scale — model deployment, prompt versioning, A/B testing, performance monitoring, drift detection, and cost optimization.
- Design human-in-the-loop escalation paths for critical AI-driven decisions, ensuring appropriate oversight.
- Lead AI platform evaluation and integration, including TCO analysis, data residency, and SLA requirements for agentic frameworks.
- Set software engineering best practices — code review standards, design patterns, technical debt management, and documentation.
- Advocate for and lead adoption of data mesh and data-as-a-product principles.
- Mentor the engineering team on data engineering, data modeling, and AI best practices.
- Perform other assigned job-related duties that align with our organization's vision, mission, and values and fall within your scope of practice.
Qualifications
Education: Bachelor's Degree or relevant experience.
Preferred Certification(s): AWS Solutions Architect Professional, Google Professional Data Engineer, Azure Solutions Architect Expert, Databricks Certified Data Engineer Professional, or equivalent.
Experience: 5+ years of relevant and practical experience.
Special Skills
- Expert proficiency in Python and SQL for data engineering at scale.
- Expertise in modern data platforms (Databricks, Snowflake, BigQuery), lakehouse architectures (Delta Lake, Iceberg), and streaming (Kafka, Flink, Pub/Sub).
- Deep expertise in at least one major cloud platform with cross-cloud awareness.
- Mastery of orchestration, transformation (dbt), containerization (Docker, K8s), and IaC (Terraform).
- Advanced enterprise data modeling, warehousing, data contracts, and API design.
- Expertise in CI/CD, data observability, governance, data mesh, and platform reliability.
- Experience in technical roadmap ownership, build-vs-buy evaluation, and budget/capacity planning.
- Expert-level knowledge of agentic AI architectures, LLMOps, RAG, knowledge graphs, and AI governance/safety/security.
Soft Skills
- Leadership: Owning and driving data and AI strategy across the organization.
- Strategic Vision: Translating business objectives into actionable technical roadmaps.
- Stakeholder Management: Building relationships with partners and executive leadership.
- Communication: Presenting complex data and AI concepts to board-level audiences.
- Mentorship: Developing the data engineering team's data and AI competencies.
- Decision-Making: Making high-impact choices on architecture, platforms, and investments.
- Change Management: Guiding the organization through data and AI transformations.
- Innovation & Thought Leadership: Driving industry best practices in data engineering, modeling, and agentic AI.
- Negotiation: Balancing technical requirements with business needs and resource constraints.
Relocation Assistance Eligible:
No
Work Shift:
1ST SHIFT (United States of America)
Certain roles at Tyson require background checks. If you are offered a position that requires a background check you will be provided additional documentation to complete once an offer has been extended.
Hourly Applicants ONLY - You must complete the task after submitting your application to provide additional information to be considered for employment.
Tyson is an Equal Opportunity Employer. All qualified applicants will be considered without regard to race, national origin, color, religion, age, genetics, sex, sexual orientation, gender identity, disability or veteran status.
We provide our team members and their families with paid time off; 401(k) plans; affordable health, life, dental, vision and prescription drug benefits; and more.
If you would like to learn more about your data privacy rights and how you may use that information, please read our Job Applicant Privacy Notice here.
Unsolicited Assistance: Tyson Foods and its subsidiaries do not accept unsolicited support from external recruitment vendors for open positions within the United States. Any resumes or candidate profiles submitted by recruitment vendors or headhunters to any employee or applicant tracking system at Tyson Foods or its subsidiaries, without a valid written request and search agreement approved by HR, will be considered the property of Tyson Foods. No fees will be paid if the candidate is hired due to an unsolicited referral.
See all 116+ Data Engineer jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Data Engineer roles.
Get Access To All JobsTips for Finding Visa Sponsorship as a Data Engineer
Verify your degree field matches the role
The H-1B1 visa requires a specialty occupation, meaning your degree must align directly with data engineering work. A computer science or information systems degree satisfies this clearly; an unrelated bachelor's paired with work experience alone does not.
Pull your O*NET occupation profile early
Data Engineer appears in O*NET under a specific SOC code that employers reference when drafting the Labor Condition Application. Knowing your SOC code lets you confirm your job duties match what DOL expects before you negotiate your offer letter.
Target employers with active LCA filing history
Search for employers who have previously filed Labor Condition Applications for data engineering roles. Migrate Mate filters job listings by verified H-1B1 Chile LCA history, so you're applying to companies already familiar with the sponsorship process.
Ask whether the employer uses E-Verify before accepting
H-1B1 Chile holders are authorized to start work once the employer files the LCA and you receive your visa stamp. Confirm the company is enrolled in E-Verify before your start date, since employment verification happens at onboarding, not at consulate approval.
Get prevailing wage confirmed before the LCA is filed
Your employer must certify your offered wage meets the DOL prevailing wage for your role and location. Run the OFLC Wage Search yourself using your SOC code and work city so you can flag any discrepancy before the LCA is submitted.
Time your consulate appointment around LCA certification
The LCA must be certified by DOL before you can schedule your consulate interview. DOL typically certifies within seven business days, so coordinate with your employer on timing to avoid booking an appointment before that step completes.
Frequently Asked Questions
Does a Data Engineer role qualify as a specialty occupation for the H-1B1 Chile visa?
Yes. Data Engineer is classified as a specialty occupation because the role requires at least a bachelor's degree in computer science, information systems, or a directly related field. The employer confirms this in the Labor Condition Application. Roles that list a degree as preferred rather than required can create complications, so reviewing the job description before accepting an offer is important.
How does the H-1B1 Chile visa compare to H-1B for Data Engineer jobs?
The H-1B1 Chile has no lottery, no USCIS petition, and the 1,400-visa annual cap has not been exhausted in recent years. For Data Engineers, this means you can secure a role and begin the consulate process without waiting for a lottery result. The trade-off is that H-1B1 Chile does not permit dual intent, so long-term permanent residence planning requires a separate strategy.
Can I switch Data Engineer employers while on an H-1B1 Chile visa?
Yes, but unlike the H-1B visa portability rule, the H-1B1 Chile does not allow you to start working for a new employer while a transfer petition is pending. Your new employer must file a fresh LCA, and you typically need to return to the consulate for a new visa stamp before beginning work. Coordinating the timing of your last day with your new employer's LCA certification is essential.
How do I find Data Engineer employers who already know how to sponsor the H-1B1 Chile visa?
Migrate Mate lists Data Engineer jobs filtered by employers with verified H-1B1 Chile LCA filing history, so you can identify companies that have already navigated the sponsorship process. This reduces the risk of an employer agreeing to sponsor and then discovering the filing requirements mid-process. Focus on employers in cloud infrastructure, analytics, and enterprise software, where demand for data engineering is consistent.
What documents should a Chilean Data Engineer prepare before the consulate interview?
You'll need your certified Labor Condition Application, an offer letter specifying your role and wage, academic transcripts and your degree certificate, a valid Chilean passport, the DS-160 confirmation, and the visa interview appointment confirmation. If your degree is not from a Chilean or U.S. institution, a credential evaluation from a USCIS-recognized evaluator is typically required to confirm equivalency.
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