AI Data Engineer Jobs at Google with Visa Sponsorship
AI Data Engineer jobs at Google sit at the intersection of large-scale data infrastructure and machine learning systems, covering pipelines, feature engineering, and model deployment at significant scale. Google has a consistent track record of sponsoring work visas for this function, including H-1B visa, H-1B1 visa, and E-3 visa classifications.
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Google Cloud Platform (GCP) offers a suite of products ranging from Compute to Storage and ML. Operating at the forefront of AI using next-generation technology, our Cloud Analytics team focuses on understanding platform resources to provide analytical insights, improve product excellence, and support scalable growth. In this role, a primary focus of your work will involve product analytics for GCP's consumption products, such as Spot and Dynamic Workload Scheduler (DWS), analyzing and improving VM reliability to optimize overall resource usage and efficiency. Additionally, you will contribute to high-visibility projects, working cross-functionally with the Google Maps Platform (GMP) team on agent analytics while partnering with core engineering and product groups. To achieve this, you will analyze complex system logs, define key performance and reliability metrics, and apply rigorous statistical methodologies to draw insights that guide infrastructure and engineering decisions. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $147000 - $211000 (USD) + 15% bonus target + equity + benefits
MINIMUM QUALIFICATIONS
- Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
- 3 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a PhD degree.
PREFERRED QUALIFICATIONS
- 5 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a PhD degree.
Responsibilities
- Lead analytics for cloud consumption products, such as Spot and DWS, focusing on improving VM reliability and optimizing resource efficiency.
- Collaborate on High-Visibility GMP Initiatives, work cross-functionally with the Google Maps Platform (GMP) engineering and product teams to develop and enhance agent analytics.
- Analyze complex system logs and apply rigorous statistical methodologies and next-generation AI technologies to extract insights.
- Establish core performance and reliability metrics to guide strategic infrastructure, product, and engineering decisions.
Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form.
ABOUT THE JOB
Google Cloud Platform (GCP) offers a suite of products ranging from Compute to Storage and ML. Operating at the forefront of AI using next-generation technology, our Cloud Analytics team focuses on understanding platform resources to provide analytical insights, improve product excellence, and support scalable growth. In this role, a primary focus of your work will involve product analytics for GCP's consumption products, such as Spot and Dynamic Workload Scheduler (DWS), analyzing and improving VM reliability to optimize overall resource usage and efficiency. Additionally, you will contribute to high-visibility projects, working cross-functionally with the Google Maps Platform (GMP) team on agent analytics while partnering with core engineering and product groups. To achieve this, you will analyze complex system logs, define key performance and reliability metrics, and apply rigorous statistical methodologies to draw insights that guide infrastructure and engineering decisions. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $147000 - $211000 (USD) + 15% bonus target + equity + benefits
MINIMUM QUALIFICATIONS
- Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
- 3 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a PhD degree.
PREFERRED QUALIFICATIONS
- 5 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a PhD degree.
Responsibilities
- Lead analytics for cloud consumption products, such as Spot and DWS, focusing on improving VM reliability and optimizing resource efficiency.
- Collaborate on High-Visibility GMP Initiatives, work cross-functionally with the Google Maps Platform (GMP) engineering and product teams to develop and enhance agent analytics.
- Analyze complex system logs and apply rigorous statistical methodologies and next-generation AI technologies to extract insights.
- Establish core performance and reliability metrics to guide strategic infrastructure, product, and engineering decisions.
Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form.
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Get Access To All JobsTips for Finding AI Data Engineer Jobs at Google
Align your portfolio to Google's stack
Google's AI Data Engineer roles prioritize hands-on experience with distributed systems, BigQuery, Vertex AI, and TensorFlow. Structure your portfolio around end-to-end ML pipelines and large-scale data processing to match what their engineering teams are actively building.
Target roles listed under specific product teams
Google posts AI Data Engineer positions across Google Cloud, DeepMind, and Google Research. Applying to team-specific openings rather than generic postings signals a clearer fit and gets your application to hiring managers closer to the actual work.
Confirm your visa type before the offer stage
Google sponsors H-1B, H-1B1 visa, and E-3 visas. Knowing which classification applies to your nationality before final-round interviews helps you respond clearly when compensation and start-date conversations begin, avoiding delays in the offer process.
Prepare for H-1B cap timing if you're cap-subject
If you need a cap-subject H-1B, USCIS registration opens in March for an October 1 start date. Receiving an offer in Q4 or Q1 means you'll likely wait through a full lottery cycle, so negotiate start dates and interim work authorization options with your recruiter early.
Use Migrate Mate to filter verified AI Data Engineer openings
Identifying which Google roles are actively open to visa sponsorship takes real research. Use Migrate Mate to filter AI Data Engineer jobs at Google by visa type, so you're applying to roles where sponsorship is confirmed rather than assumed.
Request clarity on LCA filing and prevailing wage tier
Google files a Labor Condition Application with the DOL before your H-1B petition can proceed. Ask your recruiter which wage level the role is certified at, since Level I and Level II certifications can affect the offer structure and your petition's approvability.
Frequently Asked Questions
Does Google sponsor H-1B visas for AI Data Engineers?
Yes, Google sponsors H-1B visas for AI Data Engineer roles. They also sponsor H-1B1 visas for Chilean and Singaporean nationals and E-3 visas for Australian nationals. The specific classification depends on your citizenship. Google's legal and HR teams manage the petition process internally, so your recruiter will coordinate the filing once you have a confirmed offer.
Which visa types does Google commonly use for AI Data Engineer roles?
Google uses the H-1B for most international AI Data Engineers, the H-1B1 visa for Chilean and Singaporean nationals, and the E-3 exclusively for Australian citizens. AI Data Engineer roles qualify as specialty occupations under USCIS guidelines because they require at minimum a bachelor's degree in computer science, data engineering, or a closely related field.
How do I apply for AI Data Engineer jobs at Google?
Applications go through Google's careers portal at careers.google.com. Search for AI Data Engineer roles filtered by location and team. The process typically involves an initial recruiter screen, followed by technical phone interviews covering data systems and ML infrastructure, then a virtual on-site loop. You can also browse open, visa-sponsorship-confirmed AI Data Engineer positions at Google through Migrate Mate before applying directly.
What qualifications does Google expect for AI Data Engineer roles?
Google expects a bachelor's or master's degree in computer science, data engineering, or a related technical field. Beyond credentials, strong candidates demonstrate production-level experience with distributed data pipelines, proficiency in Python or SQL, and familiarity with ML workflow tooling. For more senior levels, prior work on feature stores, model serving infrastructure, or real-time data systems is a practical differentiator.
How do I plan my timeline if I need Google to sponsor my visa?
Timeline depends on your visa type. E-3 and H-1B1 visas have no annual cap and can be filed throughout the year, so start dates are more flexible. Cap-subject H-1B petitions require USCIS registration in March, with employment beginning no earlier than October 1. If you're cap-subject, factor in a six-to-twelve month gap between offer and start date, and confirm whether Google offers cap-gap or OPT extension support during that window.