OPT Data Engineer Jobs
Data Engineer roles are among the most OPT-friendly positions in tech, with strong employer demand for F-1 students skilled in Python, SQL, and cloud platforms like AWS or Azure. Most roles qualify as STEM OPT extensions, giving you up to three years of authorized work without H-1B visa sponsorship.
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Get Access To All JobsINTRODUCTION
The Data Scientist brings deep expertise in classical AI/ML, statistics, and advanced analytics to Fairview Health Services’ AI & Automation program. The role independently frames complex problems, develops and validates data science approaches, builds solutions, and assesses models and methods developed by internal teams and external partners.
Working with clinical and operational stakeholders, engineering teams, and governance partners, the Data Scientist supports technical intake, feasibility assessment, solution design, implementation, and ongoing scientific review. The role determines whether an approach is technically sound and appropriate for its intended use, identifies limitations, recommends corrections, and communicates evidence-based technical feedback. Generative AI and LLM capabilities complement the role’s core statistical and machine-learning expertise.
This role will serve as a primary scientific and technical resource for internally developed and partner-delivered AI/ML solutions. The position will support model development, independent validation, methodology review, experimentation, and technical challenge while helping establish how Fairview evaluates model performance, limitations, bias, and ongoing scientific validity.
Responsibilities
Intake and solution design. Translate clinical and operational challenges into testable questions, data requirements, scientific approaches, and measures of success. Assess technical feasibility and recommend suitable analytical or AI/ML approaches for internal and partner initiatives.
Model development and review. Develop and validate statistical and machine-learning models, or independently review and approve models developed by others. MLOps owns production deployment and operation; partner with MLOps to transition validated models into supported use. Illustrative use cases include risk prediction, readmissions, population health, forecasting, and capacity planning.
Scientific methods validation. Design reproducible analyses, experiments, and validation plans. Assess data quality, feature selection, assumptions, validation design, performance, and limitations to determine whether results are reliable and support the proposed use.
Partner and internal assurance. Review model methods, code, data assumptions, validation evidence, and reported results delivered by internal teams and external partners. Reproduce or challenge findings as needed and recommend corrections or additional evidence.
Scientific review and approval. Independently review and approve model methods and validation evidence within assigned scientific-review authority and established governance. Document acceptance criteria, findings, limitations, and remediation; coordinate clinical, security, and production-release decisions with designated owners.
Applied data science. Develop and validate approaches to clinical and operational problems using linear and other regression models, random forests, and other suitable methods. Apply statistics to evaluate interventions, programs, and workflows; use optimization, natural-language processing, generative AI, and large language models where appropriate.
Data and implementation partnership. Define feature, cohort, outcome, and data requirements with the AI Data Modeler, data engineering, and domain owners. Build reproducible analytical code and prototypes and partner with MLOps and IT teams to implement validated solutions in supported workflows.
Ongoing scientific validity. Define model-performance measures and review criteria with scientific and domain stakeholders. Interpret monitoring findings and recommend or develop recalibration, retraining, or methodological changes; partner with MLOps on controlled implementation.
Technical leadership and communication. Independently lead the scientific work on assigned initiatives. Explain results and limitations to clinical, operational, technical, and partner audiences; provide constructive pushback, mentor colleagues, and maintain clear methodological documentation.
Privacy and governance. Follow organizational privacy, security, regulatory, and model-governance requirements. Provide documented scientific evidence supporting responsible use and informed clinical and operational decisions.
BASIC QUALIFICATIONS
- B.S./B.A. in a quantitative field such as Statistics, Computer Science, Engineering, Applied Mathematics, Data Science, Epidemiology, or Health Informatics; or a combination of education and experience equivalent to a bachelor’s degree in a quantitative field.
- Four years of data science, predictive-modeling, or advanced-analytics experience, including independent responsibility for complex analytical or scientific work.
- Demonstrated proficiency in analytical programming, advanced analytics and statistics, predictive modeling, machine learning, and data visualization.
- Strong proficiency in Python and/or R and experience working with large relational databases using SQL.
- Demonstrated experience developing and validating models or independently reviewing and validating models developed by others. Either experience route must demonstrate hands-on ability to implement analytical methods, test results, and critically assess scientific evidence.
- Deep knowledge of statistical reasoning, linear and other regression techniques, random forests, supervised and unsupervised learning, feature engineering, forecasting, model evaluation, and the selection and validation of data science methods.
- Ability to independently assess ambiguous problems, identify methodological limitations, recommend corrections, and communicate scientific and technical findings clearly.
- Ability to work effectively with clinical, operational, engineering, and external-partner teams; provide constructive technical feedback; manage multiple priorities; and support implementation of reproducible solutions.
PREFERRED QUALIFICATIONS
- M.A./M.S. in a related quantitative field.
- Experience with healthcare/EHR data, including Epic Clarity or Caboodle, and clinical-quality or operational measures.
- Experience with outcomes research, clinical research, population health, causal inference, optimization, or evaluation of interventions and workflows.
- Experience developing or evaluating natural-language processing, generative AI, or large language model solutions.
- Experience with cloud-based development, containerized solutions, computational infrastructure, modern data platforms, and data visualization.
- Knowledge of healthcare regulatory and reimbursement environments.
COMPENSATION
- $114,857.60 - $162,177.60 Annual
INTRODUCTION
The Data Scientist brings deep expertise in classical AI/ML, statistics, and advanced analytics to Fairview Health Services’ AI & Automation program. The role independently frames complex problems, develops and validates data science approaches, builds solutions, and assesses models and methods developed by internal teams and external partners.
Working with clinical and operational stakeholders, engineering teams, and governance partners, the Data Scientist supports technical intake, feasibility assessment, solution design, implementation, and ongoing scientific review. The role determines whether an approach is technically sound and appropriate for its intended use, identifies limitations, recommends corrections, and communicates evidence-based technical feedback. Generative AI and LLM capabilities complement the role’s core statistical and machine-learning expertise.
This role will serve as a primary scientific and technical resource for internally developed and partner-delivered AI/ML solutions. The position will support model development, independent validation, methodology review, experimentation, and technical challenge while helping establish how Fairview evaluates model performance, limitations, bias, and ongoing scientific validity.
Responsibilities
Intake and solution design. Translate clinical and operational challenges into testable questions, data requirements, scientific approaches, and measures of success. Assess technical feasibility and recommend suitable analytical or AI/ML approaches for internal and partner initiatives.
Model development and review. Develop and validate statistical and machine-learning models, or independently review and approve models developed by others. MLOps owns production deployment and operation; partner with MLOps to transition validated models into supported use. Illustrative use cases include risk prediction, readmissions, population health, forecasting, and capacity planning.
Scientific methods validation. Design reproducible analyses, experiments, and validation plans. Assess data quality, feature selection, assumptions, validation design, performance, and limitations to determine whether results are reliable and support the proposed use.
Partner and internal assurance. Review model methods, code, data assumptions, validation evidence, and reported results delivered by internal teams and external partners. Reproduce or challenge findings as needed and recommend corrections or additional evidence.
Scientific review and approval. Independently review and approve model methods and validation evidence within assigned scientific-review authority and established governance. Document acceptance criteria, findings, limitations, and remediation; coordinate clinical, security, and production-release decisions with designated owners.
Applied data science. Develop and validate approaches to clinical and operational problems using linear and other regression models, random forests, and other suitable methods. Apply statistics to evaluate interventions, programs, and workflows; use optimization, natural-language processing, generative AI, and large language models where appropriate.
Data and implementation partnership. Define feature, cohort, outcome, and data requirements with the AI Data Modeler, data engineering, and domain owners. Build reproducible analytical code and prototypes and partner with MLOps and IT teams to implement validated solutions in supported workflows.
Ongoing scientific validity. Define model-performance measures and review criteria with scientific and domain stakeholders. Interpret monitoring findings and recommend or develop recalibration, retraining, or methodological changes; partner with MLOps on controlled implementation.
Technical leadership and communication. Independently lead the scientific work on assigned initiatives. Explain results and limitations to clinical, operational, technical, and partner audiences; provide constructive pushback, mentor colleagues, and maintain clear methodological documentation.
Privacy and governance. Follow organizational privacy, security, regulatory, and model-governance requirements. Provide documented scientific evidence supporting responsible use and informed clinical and operational decisions.
BASIC QUALIFICATIONS
- B.S./B.A. in a quantitative field such as Statistics, Computer Science, Engineering, Applied Mathematics, Data Science, Epidemiology, or Health Informatics; or a combination of education and experience equivalent to a bachelor’s degree in a quantitative field.
- Four years of data science, predictive-modeling, or advanced-analytics experience, including independent responsibility for complex analytical or scientific work.
- Demonstrated proficiency in analytical programming, advanced analytics and statistics, predictive modeling, machine learning, and data visualization.
- Strong proficiency in Python and/or R and experience working with large relational databases using SQL.
- Demonstrated experience developing and validating models or independently reviewing and validating models developed by others. Either experience route must demonstrate hands-on ability to implement analytical methods, test results, and critically assess scientific evidence.
- Deep knowledge of statistical reasoning, linear and other regression techniques, random forests, supervised and unsupervised learning, feature engineering, forecasting, model evaluation, and the selection and validation of data science methods.
- Ability to independently assess ambiguous problems, identify methodological limitations, recommend corrections, and communicate scientific and technical findings clearly.
- Ability to work effectively with clinical, operational, engineering, and external-partner teams; provide constructive technical feedback; manage multiple priorities; and support implementation of reproducible solutions.
PREFERRED QUALIFICATIONS
- M.A./M.S. in a related quantitative field.
- Experience with healthcare/EHR data, including Epic Clarity or Caboodle, and clinical-quality or operational measures.
- Experience with outcomes research, clinical research, population health, causal inference, optimization, or evaluation of interventions and workflows.
- Experience developing or evaluating natural-language processing, generative AI, or large language model solutions.
- Experience with cloud-based development, containerized solutions, computational infrastructure, modern data platforms, and data visualization.
- Knowledge of healthcare regulatory and reimbursement environments.
COMPENSATION
- $114,857.60 - $162,177.60 Annual
See all 1,422+ OPT Data Engineer Jobs
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Get Access To All JobsTips for Finding OPT Sponsorship as a Data Engineer
Lead with your STEM OPT eligibility upfront
Data Engineering is a STEM-designated field, which means you qualify for a 24-month OPT extension beyond your initial 12 months. Mention this in your cover letter so employers know they have three years before needing to sponsor an H-1B visa.
Build a portfolio around real data pipelines
Employers hiring data engineers want proof you can build and maintain production-grade pipelines. Publish GitHub projects showing ETL workflows, data modeling, or orchestration with tools like Airflow or dbt to demonstrate hands-on capability beyond coursework.
Target companies with active H-1B filing histories
Companies that regularly file H-1B petitions for data roles are far more likely to sponsor you after OPT. DOL disclosure data shows which employers have historically sponsored data engineering positions, so prioritize applications there.
Emphasize cloud certifications to stand out
AWS Certified Data Engineer, Google Professional Data Engineer, or Azure Data Engineer certifications signal to hiring managers that you can contribute immediately. Certifications often offset concerns about sponsorship timelines by demonstrating high technical readiness.
Address OPT timing proactively with recruiters
Recruiters often assume OPT authorization is more complicated than it is. Prepare a one-sentence explanation of your current OPT status, remaining duration, and STEM extension eligibility so hiring conversations move forward without confusion or hesitation.
Apply to mid-size tech and data-driven companies
Startups and mid-size companies in fintech, healthtech, and SaaS often have urgent data engineering needs and more flexible hiring processes. They're frequently more willing to sponsor OPT workers than large enterprises with rigid HR policies.
Data Engineer OPT: Frequently Asked Questions
Does a Data Engineer role qualify for the STEM OPT extension?
Yes. Data Engineering falls under CIP codes recognized as STEM-eligible, which means most F-1 students graduating from a qualifying program in computer science, information systems, or a related field can apply for the 24-month STEM OPT extension. Your role must be directly related to your degree field, and your employer must enroll in E-Verify to support the extension.
How do I find Data Engineer jobs that explicitly support OPT work authorization?
Migrate Mate filters job listings specifically for OPT-eligible candidates, so you can browse Data Engineer roles from employers already open to F-1 work authorization without screening out irrelevant listings. This saves significant time compared to parsing general job postings that rarely specify sponsorship willingness upfront.
Can I work as a Data Engineer contractor or on a project basis during OPT?
OPT requires that your employment be directly related to your degree field, but it does not require a full-time permanent position. Contract and project-based data engineering roles can qualify, provided each engagement is substantive and field-related. Self-employment is permitted under OPT if you can demonstrate you are actively engaged in work related to your degree.
What happens to my OPT if my Data Engineer job ends before my authorization period expires?
F-1 OPT students are allowed up to 90 days of unemployment during the initial OPT period, and up to 150 days during the STEM OPT extension. If your data engineering role ends, you must actively pursue new employment in your field. Exceeding the unemployment limit puts your status at risk, so tracking your dates carefully from the day employment ends is critical.
Do Data Engineer employers need to do anything special to hire me on OPT?
For standard OPT, employers do not need to file any immigration petition or take formal action. You simply present your EAD card as proof of work authorization. For the STEM OPT extension, your employer must be enrolled in E-Verify and complete a formal training plan with your designated school official. Most established tech companies already meet this requirement.