AI Data Engineer Visa Sponsorship Jobs in Washington
Washington state is one of the most active markets for AI data engineer visa sponsorship in the country. Microsoft, Amazon, Google, and Meta all maintain major engineering operations in the Seattle metro area, with additional demand from companies like Expedia, Tableau, and T-Mobile. Bellevue and Redmond round out Seattle as the primary hiring corridor for this role.
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We are seeking a principal-level (L7) leader to own and drive the Enterprise Data Strategy, AI, and New Datamart vision across our ERP-centric technology landscape. This role spans enterprise platforms including SAP S/4HANA, Oracle Cloud Applications and similar ERP ecosystems leveraging modern data platforms such as SAP Datasphere, Oracle Analytics Cloud, Databricks, Amazon Redshift, or equivalent to build a unified, intelligent data fabric.
This leader will serve as the strategic bridge between enterprise data architecture, advanced analytics, and business intelligence — translating complex, multi-Enterprise application data landscapes into actionable, AI-powered insights at scale. The ideal candidate is platform-fluent but platform-agnostic, able to design data strategies that harness the best of any enterprise application ecosystem while maintaining a clean, governed, and extensible data foundation.
A critical mandate of this role is to establish and chair a Data Governance Council within Manufacturing Operations, driving cross-functional alignment across Engineering, Manufacturing, Supply Chain, and Finance to ensure data integrity, standardization, and actionable intelligence throughout the manufacturing value chain.
Key job responsibilities
Enterprise Data Strategy & Datamart Architecture
- Define and own the enterprise data strategy across ERP-centric environments (SAP, Oracle, or similar), establishing the roadmap for modernizing legacy data warehouses into cloud-native datamarts.
- Design and implement a new Datamart architecture leveraging platforms such as SAP Datasphere, Amazon Redshift, Aurora, unifying ERP and non-ERP data through virtualization, replication, or hybrid approaches.
- Establish the semantic layer and business data fabric that preserves business context across disparate enterprise systems, enabling consistent metrics, KPIs, and definitions across all functional domains (Finance, Supply Chain, Manufacturing, Engineering, Order-to-Cash, Procurement).
- Lead Data Modeling & Semantic Layer design — defining reusable business terms, metrics, relationships, and associations that support analytics, planning, and AI/ML initiatives regardless of the underlying ERP platform.
- Architect cloud data warehousing, data marts, and data pipelines & orchestration to ensure scalable, performant, and governed data delivery from multiple ERP sources.
Own Data Quality & Governance frameworks ensuring data integrity, lineage, certification, and lifecycle management across all enterprise datamarts and ERP systems — with particular emphasis on Manufacturing Operations data standards.
Manufacturing Operations Data Governance Council
This role is accountable for chartering, establishing, and chairing a Data Governance Council within Manufacturing Operations. The council will drive cross-functional data alignment and decision-making across key operational domains:
- Charter and launch the Manufacturing Operations Data Governance Council, defining its mission, scope, membership, decision rights, escalation paths, and cadence of reviews.
- Collaborate with Engineering to standardize product data definitions, BOM structures, engineering change order data flows, and design-to-manufacturing data handoffs.
- Partner with Manufacturing to govern production data (MES, quality, yield, OEE), enforce data standards across shop-floor systems, and enable real-time manufacturing analytics.
- Align with Supply Chain on demand planning data, inventory master data, logistics and fulfillment metrics, and end-to-end supply chain visibility through governed data pipelines.
- Coordinate with Finance to ensure manufacturing cost data, variance analysis, standard costing, and COGS reporting are underpinned by trusted, governed data from operational systems.
- Define and enforce cross-functional data standards, data ownership models, stewardship roles, and data quality SLAs across all council-participating functions.
- Establish a data issue resolution framework with clear escalation paths and accountability, ensuring disputes over data definitions, ownership, and quality are resolved efficiently.
- Report governance health metrics to senior leadership, including data quality scorecards, policy compliance rates, and council effectiveness KPIs.
Enterprise Reporting & Analytics
- Set the strategic direction for ERP-native reporting capabilities — including SAP Embedded Analytics (Fiori), Oracle OTBI/BI Publisher, or equivalent built-in reporting tools.
- Drive the evolution of Enterprise Analytics & BI leveraging platforms such as SAP Analytics Cloud (SAC), Oracle Analytics Cloud (OAC), Amazon QuickSight, Tableau, or Power BI for dashboards, scorecards, planning, predictive analytics, and self-service analytics.
- Define and implement KPI Frameworks and Data Visualization standards, enabling insight-to-action workflows across the enterprise independent of the source ERP system.
- Champion User Enablement & Adoption designing programs that democratize data access and empower business users with self-service analytics capabilities across all ERP platforms.
- Standardize operational reporting, ad-hoc reporting, and embedded analytics patterns that work consistently whether the source system is SAP, Oracle, or a third-party application.
AI & Advanced Analytics
- Identify, prioritize, and deliver Generative AI Use Cases for Enterprise Applications leveraging AI services (e.g., Amazon Bedrock, Amazon Q, SAP Joule, Oracle AI, Azure OpenAI) to embed intelligence into ERP-driven business processes.
- Build and scale Machine Learning Models for demand forecasting, supply chain optimization, financial planning, anomaly detection, and process automation across ERP platforms.
Drive Forecasting & Optimization initiatives that convert historical ERP data (from SAP, Oracle, or similar) into predictive and prescriptive insights.
- Lead Intelligent Automation efforts automating repetitive data tasks, report generation, and exception-based alerting through AI-powered workflows integrated with enterprise applications.
- Establish AI governance frameworks ensuring responsible, compliant, and explainable AI across all analytics use cases, regardless of the underlying ERP or data platform.
Integration & Platform Evolution
- Partner with Architecture & Governance teams to ensure alignment with clean core strategy, extension strategies (e.g., SAP BTP, Oracle Cloud Infrastructure, AWS), and integration standards.
- Collaborate with integration teams for data orchestration across cloud and on-premises ERP systems — managing API gateways, event-driven architectures, and ETL/ELT pipelines.
- Drive platform evolution toward modern data architectures such as SAP Business Data Cloud, Oracle Lakehouse, AWS Data Lake, Databricks Lakehouse — evaluating and road mapping the best-fit architecture for the enterprise.
- Interface with Process & Business Excellence to translate business demand into data solutions, ensuring tight alignment between business requirements and data architecture decisions across all ERP systems.
BASIC QUALIFICATIONS
- 10+ years of progressive experience in data strategy, data warehousing, business intelligence, or analytics engineering.
- 5+ years of hands-on experience with enterprise application data ecosystems across one or more of: SAP (BW, HANA, Datasphere, S/4HANA), Oracle (EBS, Fusion, Oracle Cloud, OTBI), Microsoft Dynamics 365, or similar ERP platforms.
- Deep expertise in data modeling, semantic layer design, and building enterprise datamarts that unify data across multiple ERP systems.
- Proven experience designing and delivering AI/ML solutions in enterprise environments (forecasting, optimization, NLP, or generative AI).
- Strong understanding of data governance, master data management, and data quality frameworks in multi-system environments.
- Experience establishing and leading cross-functional data governance bodies, preferably within Manufacturing or Operations environments spanning Engineering, Supply Chain, and Finance.
- Experience with cloud data platforms (AWS Redshift/Glue/Lake Formation, Databricks, Snowflake, Google BigQuery, or Azure Synapse) and modern data engineering tools.
- Track record of influencing senior leadership and driving strategic data initiatives across large, complex organizations with heterogeneous ERP landscapes.
PREFERRED QUALIFICATIONS
- Experience with modern ERP data platforms such as SAP Datasphere, Oracle Analytics Cloud, or Microsoft Fabric for enterprise data management.
- Familiarity with cloud-native ERP extensions — SAP BTP, Oracle Cloud Infrastructure, AWS, or Azure platform services.
- Experience integrating ERP data with Amazon Bedrock, Amazon Redshift, Amazon Q, or other AWS AI/analytics services.
- Background in ERP functional modules across Finance, Manufacturing, Supply Chain, Engineering, Order-to-Cash, Procurement, or Global Trade — on SAP, Oracle, or similar platforms.
- Experience with enterprise planning & analytics platforms (SAP Analytics Cloud, Oracle Planning Cloud, Adaptive Insights, Anaplan).
- Knowledge of Apache Iceberg, Delta Lake, Databricks Lakehouse, or other open data lakehouse frameworks.
- Experience managing ERP data migration and modernization programs (e.g., SAP ECC to S/4HANA, Oracle EBS to Fusion Cloud, or multi-ERP consolidation).
- Relevant certifications in enterprise data platforms (SAP, Oracle, AWS Data Analytics, Databricks, Snowflake).
- Experience standing up data governance councils in manufacturing or industrial settings with demonstrated impact on data quality and cross-functional alignment.
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
Location: USA, WA, Bellevue
Salary Range: 184,000.00 - 248,900.00 USD annually
DESCRIPTION
We are seeking a principal-level (L7) leader to own and drive the Enterprise Data Strategy, AI, and New Datamart vision across our ERP-centric technology landscape. This role spans enterprise platforms including SAP S/4HANA, Oracle Cloud Applications and similar ERP ecosystems leveraging modern data platforms such as SAP Datasphere, Oracle Analytics Cloud, Databricks, Amazon Redshift, or equivalent to build a unified, intelligent data fabric.
This leader will serve as the strategic bridge between enterprise data architecture, advanced analytics, and business intelligence — translating complex, multi-Enterprise application data landscapes into actionable, AI-powered insights at scale. The ideal candidate is platform-fluent but platform-agnostic, able to design data strategies that harness the best of any enterprise application ecosystem while maintaining a clean, governed, and extensible data foundation.
A critical mandate of this role is to establish and chair a Data Governance Council within Manufacturing Operations, driving cross-functional alignment across Engineering, Manufacturing, Supply Chain, and Finance to ensure data integrity, standardization, and actionable intelligence throughout the manufacturing value chain.
Key job responsibilities
Enterprise Data Strategy & Datamart Architecture
- Define and own the enterprise data strategy across ERP-centric environments (SAP, Oracle, or similar), establishing the roadmap for modernizing legacy data warehouses into cloud-native datamarts.
- Design and implement a new Datamart architecture leveraging platforms such as SAP Datasphere, Amazon Redshift, Aurora, unifying ERP and non-ERP data through virtualization, replication, or hybrid approaches.
- Establish the semantic layer and business data fabric that preserves business context across disparate enterprise systems, enabling consistent metrics, KPIs, and definitions across all functional domains (Finance, Supply Chain, Manufacturing, Engineering, Order-to-Cash, Procurement).
- Lead Data Modeling & Semantic Layer design — defining reusable business terms, metrics, relationships, and associations that support analytics, planning, and AI/ML initiatives regardless of the underlying ERP platform.
- Architect cloud data warehousing, data marts, and data pipelines & orchestration to ensure scalable, performant, and governed data delivery from multiple ERP sources.
Own Data Quality & Governance frameworks ensuring data integrity, lineage, certification, and lifecycle management across all enterprise datamarts and ERP systems — with particular emphasis on Manufacturing Operations data standards.
Manufacturing Operations Data Governance Council
This role is accountable for chartering, establishing, and chairing a Data Governance Council within Manufacturing Operations. The council will drive cross-functional data alignment and decision-making across key operational domains:
- Charter and launch the Manufacturing Operations Data Governance Council, defining its mission, scope, membership, decision rights, escalation paths, and cadence of reviews.
- Collaborate with Engineering to standardize product data definitions, BOM structures, engineering change order data flows, and design-to-manufacturing data handoffs.
- Partner with Manufacturing to govern production data (MES, quality, yield, OEE), enforce data standards across shop-floor systems, and enable real-time manufacturing analytics.
- Align with Supply Chain on demand planning data, inventory master data, logistics and fulfillment metrics, and end-to-end supply chain visibility through governed data pipelines.
- Coordinate with Finance to ensure manufacturing cost data, variance analysis, standard costing, and COGS reporting are underpinned by trusted, governed data from operational systems.
- Define and enforce cross-functional data standards, data ownership models, stewardship roles, and data quality SLAs across all council-participating functions.
- Establish a data issue resolution framework with clear escalation paths and accountability, ensuring disputes over data definitions, ownership, and quality are resolved efficiently.
- Report governance health metrics to senior leadership, including data quality scorecards, policy compliance rates, and council effectiveness KPIs.
Enterprise Reporting & Analytics
- Set the strategic direction for ERP-native reporting capabilities — including SAP Embedded Analytics (Fiori), Oracle OTBI/BI Publisher, or equivalent built-in reporting tools.
- Drive the evolution of Enterprise Analytics & BI leveraging platforms such as SAP Analytics Cloud (SAC), Oracle Analytics Cloud (OAC), Amazon QuickSight, Tableau, or Power BI for dashboards, scorecards, planning, predictive analytics, and self-service analytics.
- Define and implement KPI Frameworks and Data Visualization standards, enabling insight-to-action workflows across the enterprise independent of the source ERP system.
- Champion User Enablement & Adoption designing programs that democratize data access and empower business users with self-service analytics capabilities across all ERP platforms.
- Standardize operational reporting, ad-hoc reporting, and embedded analytics patterns that work consistently whether the source system is SAP, Oracle, or a third-party application.
AI & Advanced Analytics
- Identify, prioritize, and deliver Generative AI Use Cases for Enterprise Applications leveraging AI services (e.g., Amazon Bedrock, Amazon Q, SAP Joule, Oracle AI, Azure OpenAI) to embed intelligence into ERP-driven business processes.
- Build and scale Machine Learning Models for demand forecasting, supply chain optimization, financial planning, anomaly detection, and process automation across ERP platforms.
Drive Forecasting & Optimization initiatives that convert historical ERP data (from SAP, Oracle, or similar) into predictive and prescriptive insights.
- Lead Intelligent Automation efforts automating repetitive data tasks, report generation, and exception-based alerting through AI-powered workflows integrated with enterprise applications.
- Establish AI governance frameworks ensuring responsible, compliant, and explainable AI across all analytics use cases, regardless of the underlying ERP or data platform.
Integration & Platform Evolution
- Partner with Architecture & Governance teams to ensure alignment with clean core strategy, extension strategies (e.g., SAP BTP, Oracle Cloud Infrastructure, AWS), and integration standards.
- Collaborate with integration teams for data orchestration across cloud and on-premises ERP systems — managing API gateways, event-driven architectures, and ETL/ELT pipelines.
- Drive platform evolution toward modern data architectures such as SAP Business Data Cloud, Oracle Lakehouse, AWS Data Lake, Databricks Lakehouse — evaluating and road mapping the best-fit architecture for the enterprise.
- Interface with Process & Business Excellence to translate business demand into data solutions, ensuring tight alignment between business requirements and data architecture decisions across all ERP systems.
BASIC QUALIFICATIONS
- 10+ years of progressive experience in data strategy, data warehousing, business intelligence, or analytics engineering.
- 5+ years of hands-on experience with enterprise application data ecosystems across one or more of: SAP (BW, HANA, Datasphere, S/4HANA), Oracle (EBS, Fusion, Oracle Cloud, OTBI), Microsoft Dynamics 365, or similar ERP platforms.
- Deep expertise in data modeling, semantic layer design, and building enterprise datamarts that unify data across multiple ERP systems.
- Proven experience designing and delivering AI/ML solutions in enterprise environments (forecasting, optimization, NLP, or generative AI).
- Strong understanding of data governance, master data management, and data quality frameworks in multi-system environments.
- Experience establishing and leading cross-functional data governance bodies, preferably within Manufacturing or Operations environments spanning Engineering, Supply Chain, and Finance.
- Experience with cloud data platforms (AWS Redshift/Glue/Lake Formation, Databricks, Snowflake, Google BigQuery, or Azure Synapse) and modern data engineering tools.
- Track record of influencing senior leadership and driving strategic data initiatives across large, complex organizations with heterogeneous ERP landscapes.
PREFERRED QUALIFICATIONS
- Experience with modern ERP data platforms such as SAP Datasphere, Oracle Analytics Cloud, or Microsoft Fabric for enterprise data management.
- Familiarity with cloud-native ERP extensions — SAP BTP, Oracle Cloud Infrastructure, AWS, or Azure platform services.
- Experience integrating ERP data with Amazon Bedrock, Amazon Redshift, Amazon Q, or other AWS AI/analytics services.
- Background in ERP functional modules across Finance, Manufacturing, Supply Chain, Engineering, Order-to-Cash, Procurement, or Global Trade — on SAP, Oracle, or similar platforms.
- Experience with enterprise planning & analytics platforms (SAP Analytics Cloud, Oracle Planning Cloud, Adaptive Insights, Anaplan).
- Knowledge of Apache Iceberg, Delta Lake, Databricks Lakehouse, or other open data lakehouse frameworks.
- Experience managing ERP data migration and modernization programs (e.g., SAP ECC to S/4HANA, Oracle EBS to Fusion Cloud, or multi-ERP consolidation).
- Relevant certifications in enterprise data platforms (SAP, Oracle, AWS Data Analytics, Databricks, Snowflake).
- Experience standing up data governance councils in manufacturing or industrial settings with demonstrated impact on data quality and cross-functional alignment.
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
Location: USA, WA, Bellevue
Salary Range: 184,000.00 - 248,900.00 USD annually
AI Data Engineer Job Roles in Washington
See all 1,125+ AI Data Engineer Jobs in Washington
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Search AI Data Engineer Jobs in WashingtonAI Data Engineer Jobs in Washington: Frequently Asked Questions
Which companies sponsor visas for AI data engineers in Washington?
Microsoft, Amazon, and Google are among the most consistent H-1B visa sponsors for AI data engineer roles in Washington, with large engineering campuses in Redmond, Seattle, and Kirkland. Mid-size tech companies like Expedia, Tableau, Snowflake, and T-Mobile also sponsor regularly. Washington's concentration of cloud and enterprise software firms makes it one of the stronger states for finding active sponsoring employers in this specialty.
Which visa types are most common for AI data engineer roles in Washington?
The H-1B is by far the most common visa for AI data engineers in Washington, given the role's strong alignment with specialty occupation requirements in computer science and data engineering. L-1B visas appear for intracompany transferees at firms like Microsoft and Amazon. Candidates with a Canadian or Mexican citizenship sometimes use TN visas under the computer systems analyst category, depending on the specific job duties.
Which cities in Washington have the most AI data engineer sponsorship jobs?
Seattle and Redmond account for the overwhelming majority of AI data engineer sponsorship opportunities in Washington. Redmond is home to Microsoft's global headquarters, while Seattle hosts Amazon's main campus along with major offices for Google, Meta, and Salesforce. Bellevue and Kirkland also have meaningful concentrations of tech employers. Outside the greater Seattle metro, sponsorship activity drops off significantly.
How to find ai data engineer visa sponsorship jobs in Washington?
Migrate Mate is built specifically for international candidates searching for visa sponsorship roles. You can filter directly for AI data engineer positions in Washington, seeing only employers who have demonstrated a history of sponsoring work visas. This saves significant time compared to sorting through general job listings where sponsorship availability is unclear. Migrate Mate focuses exclusively on sponsorship-verified opportunities, which matters when you're on a visa timeline.
Are there any Washington-specific factors AI data engineers should know about for visa sponsorship?
Washington has no state income tax, which affects the prevailing wage analysis employers conduct as part of the H-1B Labor Condition Application process. The Department of Labor sets prevailing wages for AI data engineer roles by metropolitan area, so rates in the Seattle-Bellevue-Everett area reflect one of the highest cost-of-labor markets in the country. University pipelines from the University of Washington's Paul G. Allen School of Computer Science feed a competitive local talent pool, meaning employers are selective even when sponsoring.
What is the prevailing wage for sponsored ai data engineer jobs in Washington?
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.