H-1B Visa Data Engineering Manager Jobs
Data Engineering Manager roles qualify for H-1B visa sponsorship as specialty occupations requiring a bachelor's degree or higher in computer science, information systems, or a related field. Large tech, finance, and healthcare employers file LCAs with the DOL and run H-1B petitions through USCIS for this role annually.
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Location:
Morristown, NJ
Category:
Data Engineering
Employment Type:
Full time, Hybrid
Job Ref:
R2627586-195
VP IT Management - IM04AE
We’re determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals – and to help others accomplish theirs, too. Join our team as we help shape the future.
Join The Hartford as we continue to advance data and AI capabilities across Business Insurance. We are seeking a highly accomplished Vice President of AI Data Engineering to lead the Business Insurance Data Engineering portfolio. This executive will shape the strategy, organization, and delivery of trusted, governed, scalable, and AI-ready data capabilities that enable Business Insurance priorities.
The ideal candidate brings deep Data Engineering expertise, strong Property & Casualty insurance acumen, and a record of leading large technical organizations and complex portfolios. The role requires the ability to translate business strategy into data products, modernization priorities, AI-ready data foundations, workforce capabilities, and measurable outcomes in partnership with business and technology leaders.
This is an opportunity for a strategic data leader to shape the future of Business Insurance by strengthening the data foundations required for analytics, automation, machine learning, generative AI, and emerging agentic AI capabilities.
This role can have a Hybrid work arrangement and can be based in Hartford, CT or the NYC/NJ metro area. Must be eligible to work in the US without company sponsorship.
Key Responsibilities
- Define and champion the Business Insurance AI Data Engineering strategy and operating model, translating business priorities into multi-year portfolio plans, capability roadmaps, workforce requirements, and measurable outcomes.
- Set priorities and allocate resources across data products, modernization, AI-ready data foundations, technical debt, operational resilience, and emerging business needs.
- Ensure delivery and ongoing operation of trusted, reusable, and scalable data products, pipelines, semantic assets, and data foundations supporting analytics, automation, machine learning, generative AI, RAG, and agentic AI.
- Lead the Business Insurance agentic analytics strategy, enabling governed AI agents and decision-support experiences that can discover, interpret, reason over, and act on trusted enterprise data while maintaining human accountability for material business decisions.
- Build and scale enterprise data intelligence layers that combine business semantics, contextual metadata, ontologies, knowledge graphs, metrics, policies, lineage, and access controls so analytics and AI consistently interpret Business Insurance data in the right business context.
- Establish reusable semantic layers and governed business metrics across underwriting, pricing, distribution, servicing, claims, risk, customer, operations, and finance, reducing inconsistent definitions and accelerating trusted self-service and AI-assisted analysis.
- Partner with Analytics, AI, Architecture, Product, Governance, and business leaders to deliver context-rich data products and retrieval patterns that ground generative and agentic AI in authoritative data, approved definitions, and traceable evidence.
- Define the operating model and controls for agentic data and analytics, including agent access, identity, authorization, observability, evaluation, audit trails, feedback loops, exception handling, and lifecycle management.
- Connect Data Engineering capabilities to outcomes across underwriting, pricing, distribution, servicing, claims, risk management, customer, operational, and financial priorities.
- Guide the evolution of structured and unstructured data capabilities, including metadata, semantic assets, contextual data, embeddings, vector and graph technologies, retrieval and grounding data, and governed external data sources.
- Establish expectations for data quality, testing, lineage, traceability, observability, reliability, lifecycle management, data contracts, service levels, and consumer accountability.
- Champion responsible use of AI-assisted engineering to improve productivity, quality, testing, documentation, and delivery speed while maintaining appropriate human oversight and controls.
- Lead a large, complex organization through senior leaders, strengthening leadership effectiveness, organizational health, succession, talent development, and long-term capability sustainability.
- Own portfolio delivery, operational performance, cost effectiveness, workforce outcomes, risk management, control maturity, audit readiness, and value realization.
- Partner with business and technical executives to align priorities, architecture direction, platform capabilities, governance requirements, dependencies, and outcomes while maintaining clear Data Engineering accountability.
- Remove organizational and cross-functional barriers, guide major multidimensional initiatives, and adjust priorities based on business, financial, workforce, operational, quality, and risk insights.
- Represent Business Insurance AI Data Engineering with senior leaders, governance bodies, strategic partners, and vendors while monitoring emerging data and AI trends and their business implications.
Qualifications
- Bachelor's degree in Computer Science, Data Engineering, Information Systems, Engineering, Applied Mathematics, Business, or a related field; advanced degree preferred.
- 18+ years of applicable experience in Data Engineering, data platforms, AI-ready data ecosystems, software engineering for data-intensive systems, technology leadership, or related disciplines.
- 10+ years of progressive leadership experience, including leading senior leaders, large technical organizations, complex portfolios, or business-critical enterprise capabilities.
- Demonstrated success translating business strategy into Data Engineering strategy, operating models, portfolio priorities, resource plans, workforce capabilities, and multi-year roadmaps.
- Proven experience establishing enterprise data-product, semantic-layer, or data-intelligence capabilities that improve trusted reuse, consistent business interpretation, and measurable outcomes.
- Demonstrated leadership in enabling advanced analytics, generative AI, RAG, or agentic analytics through governed, context-rich, and traceable enterprise data foundations.
- Experience partnering across Data Engineering, Analytics, AI, Architecture, Product, Governance, Risk, and business teams to deliver shared capabilities and resolve complex ownership and operating-model decisions.
- Proven experience managing significant budgets, workforce resources, strategic vendors, modernization priorities, and portfolio performance.
- Strong business and financial acumen with the ability to connect Data and AI capabilities to measurable Business Insurance outcomes.
- Meaningful experience advising executive leaders, influencing strategic decisions, navigating complex tradeoffs, and leading organizational change, including adoption of AI-enabled ways of working.
- Exceptional executive communication, judgment, negotiation, relationship management, conflict resolution, presentation, and storytelling skills.
- Significant Property & Casualty or Business Insurance experience strongly preferred.
Technical Skills
- Mastery of modern Data Engineering and AI-ready data foundations, including data products, pipelines, data contracts, semantic and contextual data, metadata, lineage, quality, observability, reliability, testing, and lifecycle management.
- Deep expertise in enterprise data intelligence patterns that combine semantic layers, governed metrics, business glossaries, contextual metadata, ontologies, knowledge graphs, policies, lineage, and access controls.
- Strong understanding of agentic analytics architectures and operating patterns, including tool and data access, agent identity and authorization, retrieval and grounding, orchestration, memory and context, human-in-the-loop controls, evaluation, observability, auditability, exception handling, and lifecycle management.
- Deep understanding of data foundations supporting generative AI, RAG, and agentic AI, including embeddings, vector and graph technologies, retrieval structures, grounding data, trusted access, evidence traceability, and lifecycle governance.
- Demonstrated ability to design reusable semantic models and governed business metrics that support consistent analysis and AI interpretation across underwriting, pricing, distribution, servicing, claims, risk, customer, operations, and finance.
- Strong understanding of enterprise data architecture and platform concepts, with the ability to partner effectively with Architecture and Platform Engineering while maintaining accountability for Data Engineering strategy and delivery.
- Strong fluency in Python, SQL, distributed processing, Snowflake, Spark, Kafka, cloud-native technologies, orchestration, streaming, APIs, and hybrid or multi-cloud environments.
- Experience designing and operating complex data ecosystems across cloud platforms such as AWS or Google Cloud, including large-scale data integration, warehouse, lake, lakehouse, domain, API, vector, and graph capabilities.
- Strong understanding of AI-assisted engineering and the governance, human oversight, validation, traceability, workforce, and operational practices required for responsible adoption at scale.
- Deep understanding of Data Governance, Responsible AI, privacy, security, model and data risk, auditability, regulatory obligations, compliance, records management, and enterprise control frameworks.
- Demonstrated ability to evaluate technical, modernization, vendor, resource, operational, and risk tradeoffs using business, financial, technical, and regulatory context.
Compensation
The listed annualized base pay range is primarily based on analysis of similar positions in the external market. Actual base pay could vary and may be above or below the listed range based on factors including but not limited to performance, proficiency and demonstration of competencies required for the role. The base pay is just one component of The Hartford’s total compensation package for employees. Other rewards may include short-term or annual bonuses, long-term incentives, and on-the-spot recognition. The annualized base pay range for this role is:
$222,480 - $333,720
Equal Opportunity Employer/Sex/Race/Color/Veterans/Disability/Sexual Orientation/Gender Identity or Expression/Religion/Age
Job Details
Location:
Morristown, NJ
Category:
Data Engineering
Employment Type:
Full time, Hybrid
Job Ref:
R2627586-195
VP IT Management - IM04AE
We’re determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals – and to help others accomplish theirs, too. Join our team as we help shape the future.
Join The Hartford as we continue to advance data and AI capabilities across Business Insurance. We are seeking a highly accomplished Vice President of AI Data Engineering to lead the Business Insurance Data Engineering portfolio. This executive will shape the strategy, organization, and delivery of trusted, governed, scalable, and AI-ready data capabilities that enable Business Insurance priorities.
The ideal candidate brings deep Data Engineering expertise, strong Property & Casualty insurance acumen, and a record of leading large technical organizations and complex portfolios. The role requires the ability to translate business strategy into data products, modernization priorities, AI-ready data foundations, workforce capabilities, and measurable outcomes in partnership with business and technology leaders.
This is an opportunity for a strategic data leader to shape the future of Business Insurance by strengthening the data foundations required for analytics, automation, machine learning, generative AI, and emerging agentic AI capabilities.
This role can have a Hybrid work arrangement and can be based in Hartford, CT or the NYC/NJ metro area. Must be eligible to work in the US without company sponsorship.
Key Responsibilities
- Define and champion the Business Insurance AI Data Engineering strategy and operating model, translating business priorities into multi-year portfolio plans, capability roadmaps, workforce requirements, and measurable outcomes.
- Set priorities and allocate resources across data products, modernization, AI-ready data foundations, technical debt, operational resilience, and emerging business needs.
- Ensure delivery and ongoing operation of trusted, reusable, and scalable data products, pipelines, semantic assets, and data foundations supporting analytics, automation, machine learning, generative AI, RAG, and agentic AI.
- Lead the Business Insurance agentic analytics strategy, enabling governed AI agents and decision-support experiences that can discover, interpret, reason over, and act on trusted enterprise data while maintaining human accountability for material business decisions.
- Build and scale enterprise data intelligence layers that combine business semantics, contextual metadata, ontologies, knowledge graphs, metrics, policies, lineage, and access controls so analytics and AI consistently interpret Business Insurance data in the right business context.
- Establish reusable semantic layers and governed business metrics across underwriting, pricing, distribution, servicing, claims, risk, customer, operations, and finance, reducing inconsistent definitions and accelerating trusted self-service and AI-assisted analysis.
- Partner with Analytics, AI, Architecture, Product, Governance, and business leaders to deliver context-rich data products and retrieval patterns that ground generative and agentic AI in authoritative data, approved definitions, and traceable evidence.
- Define the operating model and controls for agentic data and analytics, including agent access, identity, authorization, observability, evaluation, audit trails, feedback loops, exception handling, and lifecycle management.
- Connect Data Engineering capabilities to outcomes across underwriting, pricing, distribution, servicing, claims, risk management, customer, operational, and financial priorities.
- Guide the evolution of structured and unstructured data capabilities, including metadata, semantic assets, contextual data, embeddings, vector and graph technologies, retrieval and grounding data, and governed external data sources.
- Establish expectations for data quality, testing, lineage, traceability, observability, reliability, lifecycle management, data contracts, service levels, and consumer accountability.
- Champion responsible use of AI-assisted engineering to improve productivity, quality, testing, documentation, and delivery speed while maintaining appropriate human oversight and controls.
- Lead a large, complex organization through senior leaders, strengthening leadership effectiveness, organizational health, succession, talent development, and long-term capability sustainability.
- Own portfolio delivery, operational performance, cost effectiveness, workforce outcomes, risk management, control maturity, audit readiness, and value realization.
- Partner with business and technical executives to align priorities, architecture direction, platform capabilities, governance requirements, dependencies, and outcomes while maintaining clear Data Engineering accountability.
- Remove organizational and cross-functional barriers, guide major multidimensional initiatives, and adjust priorities based on business, financial, workforce, operational, quality, and risk insights.
- Represent Business Insurance AI Data Engineering with senior leaders, governance bodies, strategic partners, and vendors while monitoring emerging data and AI trends and their business implications.
Qualifications
- Bachelor's degree in Computer Science, Data Engineering, Information Systems, Engineering, Applied Mathematics, Business, or a related field; advanced degree preferred.
- 18+ years of applicable experience in Data Engineering, data platforms, AI-ready data ecosystems, software engineering for data-intensive systems, technology leadership, or related disciplines.
- 10+ years of progressive leadership experience, including leading senior leaders, large technical organizations, complex portfolios, or business-critical enterprise capabilities.
- Demonstrated success translating business strategy into Data Engineering strategy, operating models, portfolio priorities, resource plans, workforce capabilities, and multi-year roadmaps.
- Proven experience establishing enterprise data-product, semantic-layer, or data-intelligence capabilities that improve trusted reuse, consistent business interpretation, and measurable outcomes.
- Demonstrated leadership in enabling advanced analytics, generative AI, RAG, or agentic analytics through governed, context-rich, and traceable enterprise data foundations.
- Experience partnering across Data Engineering, Analytics, AI, Architecture, Product, Governance, Risk, and business teams to deliver shared capabilities and resolve complex ownership and operating-model decisions.
- Proven experience managing significant budgets, workforce resources, strategic vendors, modernization priorities, and portfolio performance.
- Strong business and financial acumen with the ability to connect Data and AI capabilities to measurable Business Insurance outcomes.
- Meaningful experience advising executive leaders, influencing strategic decisions, navigating complex tradeoffs, and leading organizational change, including adoption of AI-enabled ways of working.
- Exceptional executive communication, judgment, negotiation, relationship management, conflict resolution, presentation, and storytelling skills.
- Significant Property & Casualty or Business Insurance experience strongly preferred.
Technical Skills
- Mastery of modern Data Engineering and AI-ready data foundations, including data products, pipelines, data contracts, semantic and contextual data, metadata, lineage, quality, observability, reliability, testing, and lifecycle management.
- Deep expertise in enterprise data intelligence patterns that combine semantic layers, governed metrics, business glossaries, contextual metadata, ontologies, knowledge graphs, policies, lineage, and access controls.
- Strong understanding of agentic analytics architectures and operating patterns, including tool and data access, agent identity and authorization, retrieval and grounding, orchestration, memory and context, human-in-the-loop controls, evaluation, observability, auditability, exception handling, and lifecycle management.
- Deep understanding of data foundations supporting generative AI, RAG, and agentic AI, including embeddings, vector and graph technologies, retrieval structures, grounding data, trusted access, evidence traceability, and lifecycle governance.
- Demonstrated ability to design reusable semantic models and governed business metrics that support consistent analysis and AI interpretation across underwriting, pricing, distribution, servicing, claims, risk, customer, operations, and finance.
- Strong understanding of enterprise data architecture and platform concepts, with the ability to partner effectively with Architecture and Platform Engineering while maintaining accountability for Data Engineering strategy and delivery.
- Strong fluency in Python, SQL, distributed processing, Snowflake, Spark, Kafka, cloud-native technologies, orchestration, streaming, APIs, and hybrid or multi-cloud environments.
- Experience designing and operating complex data ecosystems across cloud platforms such as AWS or Google Cloud, including large-scale data integration, warehouse, lake, lakehouse, domain, API, vector, and graph capabilities.
- Strong understanding of AI-assisted engineering and the governance, human oversight, validation, traceability, workforce, and operational practices required for responsible adoption at scale.
- Deep understanding of Data Governance, Responsible AI, privacy, security, model and data risk, auditability, regulatory obligations, compliance, records management, and enterprise control frameworks.
- Demonstrated ability to evaluate technical, modernization, vendor, resource, operational, and risk tradeoffs using business, financial, technical, and regulatory context.
Compensation
The listed annualized base pay range is primarily based on analysis of similar positions in the external market. Actual base pay could vary and may be above or below the listed range based on factors including but not limited to performance, proficiency and demonstration of competencies required for the role. The base pay is just one component of The Hartford’s total compensation package for employees. Other rewards may include short-term or annual bonuses, long-term incentives, and on-the-spot recognition. The annualized base pay range for this role is:
$222,480 - $333,720
Equal Opportunity Employer/Sex/Race/Color/Veterans/Disability/Sexual Orientation/Gender Identity or Expression/Religion/Age
See all 331+ H-1B Visa Data Engineering Manager Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new H-1B Visa Data Engineering Manager Jobs.
Get Access To All JobsTips for Finding H-1B Visa Sponsorship as a Data Engineering Manager
Align your credentials to specialty occupation standards
USCIS evaluates whether your degree field directly relates to data engineering management. A computer science or information systems degree is the clearest fit. If your degree is adjacent, gather course transcripts showing data systems coursework before applying.
Target employers with active LCA filing history
Search Migrate Mate to filter employers who have filed Labor Condition Applications for data engineering and management roles. This confirms they have experience running H-1B petitions for this occupation, not just engineering individual contributor positions.
Verify prevailing wage tier before evaluating offers
Use the OFLC Wage Search to look up wage levels for your job title, SOC code, and metro area. Data Engineering Manager roles often fall at Level III or IV. An offer below the certified LCA wage will fail DOL review.
Confirm your role meets O*NET management criteria
Pull the O*NET profile for database administrators and data architects to check which tasks and knowledge areas USCIS expects. Data Engineering Manager petitions are stronger when the job description maps directly to O*NET's listed tasks for the occupation.
Plan around the cap and registration window
USCIS opens H-1B registration in March for the fiscal year starting October 1. If you're not already on a valid H-1B, your employer must register you in that window. Missing it means waiting another year unless you qualify for a cap-exempt employer.
Negotiate petition filing timeline into your offer
Ask your employer to confirm whether they'll file standard or premium processing before you accept. Standard processing can run several months; premium processing through USCIS gets a decision within 15 business days. This affects your start date and status transition planning.
H-1B Visa Data Engineering Manager: Frequently Asked Questions
Does a Data Engineering Manager role qualify as an H-1B specialty occupation?
Yes. USCIS classifies Data Engineering Manager as a specialty occupation because the role typically requires at least a bachelor's degree in computer science, information systems, or a closely related field. The job description must show that a specific degree field is a standard requirement for the position, not just a preference.
Which employers sponsor H-1B visas for Data Engineering Manager positions?
Tech companies, financial institutions, healthcare systems, and large consulting firms are the most consistent H-1B sponsors for this role. Employers in industries with mature data infrastructure teams file LCAs for this occupation regularly. Use Migrate Mate to browse current openings filtered by employers with active H-1B filing history for data engineering management roles.
How does the prevailing wage requirement affect Data Engineering Manager H-1B petitions?
Your employer files an LCA with the DOL certifying your offered salary meets the prevailing wage for the role in your work location. Data Engineering Manager typically maps to a higher wage level given the management component. If the offered salary falls below the certified wage level, DOL will reject the LCA and the H-1B petition can't proceed.
Can I transfer my H-1B to a new Data Engineering Manager role at a different company?
Yes. Under H-1B portability rules, you can start working for a new employer as soon as the new petition is filed, as long as you're in valid status and your prior petition was approved. The new employer must file a fresh I-129 and LCA for the Data Engineering Manager role. You don't need to wait for approval before starting.
Does managing a team affect how USCIS evaluates the specialty occupation standard?
It can work in your favor. Petitions for managerial roles are stronger when the job description demonstrates that the management duties are secondary to specialized technical oversight, not general administration. USCIS looks for evidence that the degree requirement flows from the technical nature of the work, not just the seniority of the title.