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Data Platform Engineer roles qualify for EB-2 and EB-3 green card sponsorship through PERM labor certification, which requires your employer to document recruitment efforts before filing an I-140 petition on your behalf. Engineers with advanced degrees or specialized credentials in distributed systems, cloud infrastructure, or data pipeline architecture often qualify under EB-2.
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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 372+ Green Card Data Platform Engineer Jobs
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Get Access To All JobsTips for Finding Green Card Sponsorship as a Data Platform Engineer
Align your credentials to EB-2 requirements
A master's degree in computer science, data engineering, or a related field supports EB-2 classification. If you hold a bachelor's plus five or more years of progressive data platform experience, document each role's scope and technical depth carefully for PERM review.
Target employers with active PERM filing history
Search the OFLC Wage Search to identify companies that have filed PERM applications for data engineering roles. Employers with repeated filings have established internal workflows for sponsorship, which reduces delays caused by first-time process uncertainty.
Find green card sponsors faster using Migrate Mate
Use Migrate Mate to filter Data Platform Engineer roles by employers with green card sponsorship history. You'll see which companies have sponsored similar roles, cutting the research time you'd otherwise spend cross-referencing OFLC disclosure data manually.
Verify your SOC code before the PERM audit
PERM applications for data platform roles typically file under SOC 15-1243 or 15-1252. Check your job description against the O*NET occupation profile for the relevant code so your duties, degree requirement, and wage level align before DOL reviews the application.
Ask about prevailing wage level during offer negotiation
Your employer must pay at least the DOL-certified prevailing wage for your geographic location and SOC code. Request the wage level determination during offer discussions, since a Level I or II determination can affect your total compensation relative to the local market rate.
Sequence your I-485 filing around priority date availability
Once your I-140 is approved, your ability to file for adjustment of status depends on your country of birth and the published priority date. Most Data Platform Engineers born outside India or China can file I-485 concurrently with I-140 under current USCIS visa bulletin guidance.
Green Card Data Platform Engineer: Frequently Asked Questions
Does a Data Platform Engineer role typically qualify for EB-2 or EB-3 sponsorship?
Most Data Platform Engineer positions qualify for EB-2 if the employer requires a master's degree or equivalent, or EB-3 if the minimum requirement is a bachelor's degree. The classification depends on how the employer defines the position's minimum requirements in the PERM application, not solely on your personal credentials. If you hold an advanced degree but the employer only requires a bachelor's, the filing may default to EB-3.
How does green card sponsorship differ from H-1B sponsorship for this role?
H-1B visa is a temporary nonimmigrant status capped at 85,000 new visas annually with a lottery for most applicants. PERM-based green card sponsorship leads to permanent residency and carries no annual cap at the petition stage. The tradeoff is timeline: PERM labor certification alone can take six to twelve months before the I-140 is even filed, making green card sponsorship a longer but more permanent outcome than an H-1B approval.
How can I find Data Platform Engineer jobs that include green card sponsorship?
Migrate Mate lets you search Data Platform Engineer roles filtered by employers with verified green card sponsorship history, so you're not relying on job descriptions that vaguely mention 'visa sponsorship available.' Employers who have completed PERM filings for similar roles in the past are far more likely to have internal HR processes and legal counsel already in place for your case.
What does the PERM labor certification process require from my employer?
PERM requires your employer to conduct a DOL-supervised recruitment process to demonstrate that no qualified U.S. worker is available for the role. This includes posting the position, collecting and reviewing applications, and documenting why any U.S. applicants were not selected. The employer then files Form 9089 with DOL. The process typically takes three to eight months before DOL issues a certification decision.
Can I change jobs after my employer files my green card petition?
Yes, with important conditions. Under AC21 portability rules, if your I-485 adjustment of status application has been pending for 180 days or more and your I-140 is approved, you can change to a same or similar occupational classification without losing your priority date. Data platform and data engineering roles generally share SOC classifications, which can support portability claims, but you should verify your specific situation with a qualified immigration attorney.