Data Platform Engineer Jobs in USA with Visa Sponsorship
Data Platform Engineers build and maintain large-scale data infrastructure, making them strong candidates for H-1B visa sponsorship. The role typically requires a computer science or engineering degree and demonstrates the specialized technical knowledge that satisfies USCIS specialty occupation requirements. For detailed occupation requirements, see the O*NET profile.
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Get Access To All JobsSenior Analytics Data Platform Engineer
DoubleVerify · New York (Hybrid)
About DoubleVerify
DoubleVerify is a leading software platform for digital media measurement, data and analytics. DV's mission is to be the definitive source of transparency and data-driven insights into the quality and effectiveness of digital advertising for the world's largest brands, publishers and digital ad platforms. DV's technology platform provides advertisers with consistent and unbiased data and analytics that can be used to optimize the quality and return on their digital ad investments. Since 2008, DV has helped hundreds of Fortune 500 companies gain the most from their media spend by delivering best-in-class solutions across the digital advertising ecosystem, helping to build a better industry.
The Team
You will join the Data & Analytics Platform (DAP) team within the Pinnacle engineering organization. The DAP team owns and operates two data consolidation platforms — Quantum (contract-driven, next-gen) and Analytics 2.0 (SQL-driven, legacy) — that ingest, transform, and serve billions of records daily from social platforms, measurement systems, and third-party partners. The data powers Looker dashboards, customer-facing reports, and downstream APIs used across DoubleVerify's product suite.
What You'll Do
- Platform Abstraction & Design: Design and maintain the YAML-based "Contract" system that allows users to define data entities, transformations, and SLOs without writing low-level orchestration code.
- Infrastructure as Code (IaC): Develop the translation engine that converts user contracts into automated dbt models, Airflow DAGs, and Snowflake objects.
- API Development: Transition the platform from static configuration files to a dynamic, API-first architecture, enabling programmatic creation of data artifacts.
- Self-Service Enablement: Build tooling and guardrails that allow business units to deploy their own data solutions while maintaining global standards for governance and security.
- Performance & Scale: Optimize the "translation" layer to ensure that generated jobs are efficient, cost-effective, and leverage the full power of the Snowflake/dbt stack.
- Developer Experience (DevEx): Act as the "Product Manager" for your platform, gathering feedback from internal users to simplify the data development lifecycle.
- Design and build data pipelines that process billions of records a day across consolidation, semantic, and externalization layers using the DV Internal Data Platform — a self-service, contract-driven architecture where pipelines are defined via YAML contracts and automatically deployed to Snowflake, Airflow, and Looker.
- Develop and extend the Contract Interpreter — a Python library (Pydantic, Jinja2) that reads contract driven platform based YAML and generates dbt models, Airflow DAGs, and environment configurations for each deployment environment (dev, stg, prod).
- Lead new initiatives and integrations with the world's largest social platforms (YouTube, TikTok, Meta, Snapchat, Reddit, Netflix, etc.) to measure ad performance end-to-end.
- Build and maintain the semantic layer — design LookML models, explores, and views that translate consolidated data into customer-ready analytics through Looker.
- Implement and maintain observability — build monitoring, alerting, watermarking, and data consistency checks to ensure pipeline reliability and data freshness at scale.
- Leverage AI agents and tooling — contribute to and use the team's AI agent workspace (meta-repo with AGENTS.md context files, skills, and MCP integrations) to accelerate development, automate workflows, and encode institutional knowledge for AI-assisted engineering.
- Design schema evolution and data migration strategies — manage schema versioning, backward compatibility, incremental vs. full-refresh deployments, and large-scale data backfills.
- Work in multi-functional agile teams with end-to-end responsibility for product development and delivery — from contract definition to customer-facing data.
- Collaborate directly with engineers from partner platforms on API development and data integration specifications.
- Train and mentor a team of software engineers.
Who You Are
Required
- Bachelor's degree or foreign equivalent in Computer Science, Data Engineering, or a related field.
- 5+ years of experience in a Data Engineering or related role.
- Strong SQL skills — advanced querying, performance tuning, window functions, and complex transformations at scale.
- Proficiency in Python — building libraries, data processing scripts, and automation tooling (experience with Pydantic, Jinja2, or similar templating frameworks is a plus).
- Deep experience with Snowflake — schema design, Snowpipe, streams, tasks, materialized views, clustering, and query optimization.
- Experience with dbt (data build tool) — building and maintaining models, macros, custom materializations, and incremental strategies.
- Experience with orchestration tools — Airflow / Cloud Composer, DAG design, scheduling, and monitoring.
- Experience with cloud platforms — GCP (GCS, BigQuery, Cloud Composer, Kubernetes) or equivalent.
- Strong understanding of data warehousing concepts — dimensional modeling, star/snowflake schemas, slowly changing dimensions, fact/aggregate table design, and data consistency patterns.
- Experience with CI/CD pipelines — GitLab CI, Flyway migrations, or similar deployment automation.
- Experience with AI-assisted development tools — Claude Code, Cursor, GitHub Copilot, or similar AI coding assistants. Experience building or contributing to AI agent context files (AGENTS.md), skills, or meta-repo patterns is a strong plus.
Preferred
- Experience building or working with contract-driven / configuration-driven data platforms where pipelines are generated from declarative specifications (YAML, JSON schemas).
- Experience with Looker / LookML — building semantic models, explores, aggregate awareness, and dashboard development.
- Experience with Kafka — schema registries, topic management, and streaming data integration.
- Experience with data quality and observability frameworks — automated testing, watermarking, data integrity validation, and SLA monitoring.
- Experience with Terraform or infrastructure-as-code for managing cloud resources.
- Familiarity with data mesh principles — federated data ownership, data products, and self-service platform design.
The successful candidate's starting salary will be determined based on a number of non-discriminating factors, including qualifications for the role, level, skills, experience, location, and balancing internal equity relative to peers at DV.
The estimated salary range for this role based on the qualifications set forth in the job description is between $107,000 - $212,000. This role will also be eligible for bonus/commission (as applicable), equity, and benefits.
The range above is for the expectations as laid out in the job description; however, we are often open to a wide variety of profiles, and recognize that the person we hire may be more or less experienced than this job description as posted.
Not-so-fun fact: Research shows that while men apply to jobs when they meet an average of 60% of job criteria, women and other marginalized groups tend to only apply when they check every box. So if you think you have what it takes but you're not sure that you check every box, apply anyway!
Senior Analytics Data Platform Engineer
DoubleVerify · New York (Hybrid)
About DoubleVerify
DoubleVerify is a leading software platform for digital media measurement, data and analytics. DV's mission is to be the definitive source of transparency and data-driven insights into the quality and effectiveness of digital advertising for the world's largest brands, publishers and digital ad platforms. DV's technology platform provides advertisers with consistent and unbiased data and analytics that can be used to optimize the quality and return on their digital ad investments. Since 2008, DV has helped hundreds of Fortune 500 companies gain the most from their media spend by delivering best-in-class solutions across the digital advertising ecosystem, helping to build a better industry.
The Team
You will join the Data & Analytics Platform (DAP) team within the Pinnacle engineering organization. The DAP team owns and operates two data consolidation platforms — Quantum (contract-driven, next-gen) and Analytics 2.0 (SQL-driven, legacy) — that ingest, transform, and serve billions of records daily from social platforms, measurement systems, and third-party partners. The data powers Looker dashboards, customer-facing reports, and downstream APIs used across DoubleVerify's product suite.
What You'll Do
- Platform Abstraction & Design: Design and maintain the YAML-based "Contract" system that allows users to define data entities, transformations, and SLOs without writing low-level orchestration code.
- Infrastructure as Code (IaC): Develop the translation engine that converts user contracts into automated dbt models, Airflow DAGs, and Snowflake objects.
- API Development: Transition the platform from static configuration files to a dynamic, API-first architecture, enabling programmatic creation of data artifacts.
- Self-Service Enablement: Build tooling and guardrails that allow business units to deploy their own data solutions while maintaining global standards for governance and security.
- Performance & Scale: Optimize the "translation" layer to ensure that generated jobs are efficient, cost-effective, and leverage the full power of the Snowflake/dbt stack.
- Developer Experience (DevEx): Act as the "Product Manager" for your platform, gathering feedback from internal users to simplify the data development lifecycle.
- Design and build data pipelines that process billions of records a day across consolidation, semantic, and externalization layers using the DV Internal Data Platform — a self-service, contract-driven architecture where pipelines are defined via YAML contracts and automatically deployed to Snowflake, Airflow, and Looker.
- Develop and extend the Contract Interpreter — a Python library (Pydantic, Jinja2) that reads contract driven platform based YAML and generates dbt models, Airflow DAGs, and environment configurations for each deployment environment (dev, stg, prod).
- Lead new initiatives and integrations with the world's largest social platforms (YouTube, TikTok, Meta, Snapchat, Reddit, Netflix, etc.) to measure ad performance end-to-end.
- Build and maintain the semantic layer — design LookML models, explores, and views that translate consolidated data into customer-ready analytics through Looker.
- Implement and maintain observability — build monitoring, alerting, watermarking, and data consistency checks to ensure pipeline reliability and data freshness at scale.
- Leverage AI agents and tooling — contribute to and use the team's AI agent workspace (meta-repo with AGENTS.md context files, skills, and MCP integrations) to accelerate development, automate workflows, and encode institutional knowledge for AI-assisted engineering.
- Design schema evolution and data migration strategies — manage schema versioning, backward compatibility, incremental vs. full-refresh deployments, and large-scale data backfills.
- Work in multi-functional agile teams with end-to-end responsibility for product development and delivery — from contract definition to customer-facing data.
- Collaborate directly with engineers from partner platforms on API development and data integration specifications.
- Train and mentor a team of software engineers.
Who You Are
Required
- Bachelor's degree or foreign equivalent in Computer Science, Data Engineering, or a related field.
- 5+ years of experience in a Data Engineering or related role.
- Strong SQL skills — advanced querying, performance tuning, window functions, and complex transformations at scale.
- Proficiency in Python — building libraries, data processing scripts, and automation tooling (experience with Pydantic, Jinja2, or similar templating frameworks is a plus).
- Deep experience with Snowflake — schema design, Snowpipe, streams, tasks, materialized views, clustering, and query optimization.
- Experience with dbt (data build tool) — building and maintaining models, macros, custom materializations, and incremental strategies.
- Experience with orchestration tools — Airflow / Cloud Composer, DAG design, scheduling, and monitoring.
- Experience with cloud platforms — GCP (GCS, BigQuery, Cloud Composer, Kubernetes) or equivalent.
- Strong understanding of data warehousing concepts — dimensional modeling, star/snowflake schemas, slowly changing dimensions, fact/aggregate table design, and data consistency patterns.
- Experience with CI/CD pipelines — GitLab CI, Flyway migrations, or similar deployment automation.
- Experience with AI-assisted development tools — Claude Code, Cursor, GitHub Copilot, or similar AI coding assistants. Experience building or contributing to AI agent context files (AGENTS.md), skills, or meta-repo patterns is a strong plus.
Preferred
- Experience building or working with contract-driven / configuration-driven data platforms where pipelines are generated from declarative specifications (YAML, JSON schemas).
- Experience with Looker / LookML — building semantic models, explores, aggregate awareness, and dashboard development.
- Experience with Kafka — schema registries, topic management, and streaming data integration.
- Experience with data quality and observability frameworks — automated testing, watermarking, data integrity validation, and SLA monitoring.
- Experience with Terraform or infrastructure-as-code for managing cloud resources.
- Familiarity with data mesh principles — federated data ownership, data products, and self-service platform design.
The successful candidate's starting salary will be determined based on a number of non-discriminating factors, including qualifications for the role, level, skills, experience, location, and balancing internal equity relative to peers at DV.
The estimated salary range for this role based on the qualifications set forth in the job description is between $107,000 - $212,000. This role will also be eligible for bonus/commission (as applicable), equity, and benefits.
The range above is for the expectations as laid out in the job description; however, we are often open to a wide variety of profiles, and recognize that the person we hire may be more or less experienced than this job description as posted.
Not-so-fun fact: Research shows that while men apply to jobs when they meet an average of 60% of job criteria, women and other marginalized groups tend to only apply when they check every box. So if you think you have what it takes but you're not sure that you check every box, apply anyway!
See all 435+ Data Platform Engineer Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Data Platform Engineer roles.
Get Access To All JobsTips for Finding Visa Sponsorship as a Data Platform Engineer
Highlight distributed systems expertise
Emphasize experience with Kafka, Spark, Hadoop, or cloud data platforms. These specialized skills demonstrate the technical complexity that supports H-1B specialty occupation requirements.
Document your data architecture projects
Prepare detailed examples of data pipelines, ETL processes, or infrastructure you've designed. Concrete technical achievements help employers justify the specialized knowledge requirement.
Target companies with existing data teams
Look for employers already running large-scale data operations. They understand the specialized skills required and are more likely to sponsor visas for platform roles.
Emphasize your degree relevance
Connect your computer science, engineering, or mathematics degree directly to data platform work. USCIS looks for clear alignment between education and job requirements.
Research the company's data stack
Learn about their specific technologies before applying. Demonstrating knowledge of their infrastructure shows genuine interest and technical preparation for the specialized role.
Prepare for technical visa interviews
Be ready to explain your data engineering work in detail. Consular officers may ask technical questions to verify the specialized nature of your role.
Frequently Asked Questions
Do Data Platform Engineers qualify for H-1B visas?
Yes, Data Platform Engineers typically qualify for H-1B visas as the role requires specialized technical knowledge in distributed systems, data architecture, and engineering. The position usually demands a relevant bachelor's degree and demonstrates the complexity USCIS looks for in specialty occupations.
What degree do I need for Data Platform Engineer visa sponsorship?
A bachelor's degree in computer science, software engineering, data science, mathematics, or a closely related technical field is typically required. Some employers may accept equivalent combinations of education and experience, but a relevant degree strengthens your H-1B application significantly.
Which visa types work best for Data Platform Engineers?
H-1B is the most common path, with strong approval rates for technical roles. E-3 visas work for Australians, TN visas for Canadians and Mexicans under computer systems analyst classification. O-1 visas are possible for engineers with exceptional achievements in data infrastructure.
How to find Data Platform Engineer jobs with visa sponsorship?
To find Data Platform Engineer jobs with visa sponsorship, use Migrate Mate, which specializes in connecting international tech professionals with sponsoring employers. Focus your search on tech companies, financial services firms, and healthcare organizations that frequently sponsor H-1B, TN, and O-1 visas for data engineering roles. These employers actively seek candidates with cloud platforms, ETL pipeline, and big data expertise.
Do tech companies sponsor Data Platform Engineers?
Yes, major tech companies, data-driven startups, and enterprises with large-scale data operations frequently sponsor Data Platform Engineers. Companies like Amazon, Google, Netflix, and Uber regularly hire and sponsor these roles due to high demand for specialized data infrastructure skills.
How do I prove my Data Platform Engineer role is specialized?
Document your work with complex distributed systems, real-time data processing, or large-scale infrastructure projects. Highlight specific technologies like Kubernetes, Apache Airflow, or cloud platforms. Prepare technical examples that demonstrate the advanced engineering knowledge your role requires beyond basic programming.
What is the prevailing wage requirement for sponsored Data Platform 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.