Data Analytics Engineer Jobs in USA with Visa Sponsorship
Data analytics engineer roles are among the most consistently sponsored positions in the U.S. tech industry. Employers regularly file H-1B visa and O-1 visa petitions for this title, and a bachelor's degree in computer science, statistics, or a related field typically satisfies the specialty occupation requirement. For detailed occupation requirements, see the O*NET profile.
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Get Access To All JobsINTRODUCTION
The client is looking for a Data Engineer & Insights Analyst to join its team of analytics professionals to support the client Longitudinal Data System (LDS). This position will be responsible for transforming complex data into trusted, usable information to support program delivery, operational improvements, and executive decision-making in state government agencies. The Data Engineer & Insights Analyst will understand policy-relevant research questions, evaluate available data sources, and develop analytics products to deliver actionable insights.
Additionally, the Data Engineer & Insights Analyst will design and build data pipelines, integrate and transform data from multiple sources, identify and address data quality issues, and establish controls to support responsible use of data. This role requires both technical depth and strong analytical judgment, including the ability to work with large, complex, and confidential datasets.
The successful candidate must be self-directed and comfortable supporting the data needs of multiple teams, systems, and products. They must also be comfortable with supporting data conversions associated with system modernization and/or replacement efforts. The right candidate will be excited by the prospect of optimizing the Commonwealth's longitudinal data architecture to support our next generation of data initiatives.
Responsibilities:
- Create and maintain optimal data pipeline architecture.
- Design and implement cloud-based data solutions using AWS services such as S3, Glue, Redshift, EMR, Athena, Kinesis, and Lambda.
- Build and maintain data orchestration workflows using tools such as Apache Airflow, AWS Step Functions, or similar services.
- Assemble large, complex data sets that meet functional / non-functional business area requirements.
- Engineer analytical data environments that support advanced statistical analysis, predictive modeling, machine learning, and evidence-building activities.
- Partner with data scientists, research analysts, and policy analysts to ensure enterprise data assets support reproducible research and statistical modeling.
- Identify, design, and implement internal process improvements: e.g. automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability.
- Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and 'big data' technologies.
- Build analytics tools that utilize the data pipeline to provide actionable insights into education and workforce outcomes, operational efficiency, and other key business performance metrics.
- Develop and maintain data pipeline monitoring, alerting, and logging frameworks.
- Implement data quality checks and validation frameworks across pipelines.
- Develop dashboards, reports, analytical models, and/or other decision-support products.
- Work with stakeholders including the Executive, Data, Design, and Support teams to assist with data-related technical issues and support their data infrastructure needs.
- Ensure compliance with applicable Commonwealth policies, data governance standards, privacy requirements, security controls, and interagency data-sharing agreements.
- Create data tools for analytics and data scientist team members that assist them in building and optimizing data products needed to support ongoing operations and data-informed decision-making.
- Work with data and analytics professionals to strive for greater functionality in our data systems.
BASIC QUALIFICATIONS:
- Advanced SQL knowledge and proficiency working with relational databases, query authoring (SQL), as well as working familiarity with a variety of databases.
- Experience building and optimizing 'big data' data pipelines, architectures, and data sets.
- Advanced knowledge and proficiency designing, building, and refining dashboards, reports, analytical models, and decision-support products for executive and non-technical users.
- Experience working with student-level higher education data, including establishing linkages to other sources (e.g. employment and wage data) to measure outcomes and/or generate insights for decision-making.
- Experience performing root-cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement.
- Strong analytic skills related to working with unstructured datasets.
- Experience building processes supporting data transformation, data structures, metadata, dependency, and workload management.
- A successful history of manipulating, processing, and extracting value from large, disconnected datasets.
- Working knowledge of message queuing, stream processing, and highly scalable 'big data' data stores.
- Strong project management and organizational skills.
- Experience supporting and working with cross-functional teams in a dynamic environment.
SKILL - REQUIRED / DESIRED - AMOUNT OF EXPERIENCE
- Hands-on experience with cloud-based data services such as AWS (e.g. S3, EC2, Glue, EMR, RDS, Redshift, Athena, Lambda, DynamoDB) - Required - 5 Years
- Proficiency in object-oriented/object function scripting languages: e.g. Python, R, Java, C++, Scala - Required - 5 Years
- Proficiency in relational SQL and NoSQL databases, including Oracle, MS SQL Server, Postgres, Cassandra. - Required - 5 Years
- Experience with version control systems such as Git and CI/CD pipelines - Required - 5 Years
- Knowledge of data modeling techniques including dimensional modeling, data vault, and star/snowflake schemas. - Required - 5 Years
- Familiarity with data cataloging and metadata management tools such as AWS Glue Data Catalog. - Required - 5 Years
- Understanding of data governance, data lineage, and data quality frameworks. - Required - 5 Years
- Knowledge of REST API design and integration patterns. - Required - 5 Years
- Experience with big data tools: e.g. Hadoop, Spark, Kafka. - Required - 5 Years
- Experience with data pipeline and workflow management tools: e.g. Azkaban, Luigi, Airflow. - Required - 5 Years
- Experience with data integration services solutions from vendors such as Informatica, MuleSoft, Talend, and TIBCO. - Required - 5 Years
- Experience with stream-processing systems: e.g. Storm, Spark-Streaming. - Required - 5 Years
NOTES:
Work will be remote.
VIVA is an equal opportunity employer. All qualified applicants have an equal opportunity for placement, and all employees have an equal opportunity to develop on the job. This means that VIVA will not discriminate against any employee or qualified applicant on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status.
INTRODUCTION
The client is looking for a Data Engineer & Insights Analyst to join its team of analytics professionals to support the client Longitudinal Data System (LDS). This position will be responsible for transforming complex data into trusted, usable information to support program delivery, operational improvements, and executive decision-making in state government agencies. The Data Engineer & Insights Analyst will understand policy-relevant research questions, evaluate available data sources, and develop analytics products to deliver actionable insights.
Additionally, the Data Engineer & Insights Analyst will design and build data pipelines, integrate and transform data from multiple sources, identify and address data quality issues, and establish controls to support responsible use of data. This role requires both technical depth and strong analytical judgment, including the ability to work with large, complex, and confidential datasets.
The successful candidate must be self-directed and comfortable supporting the data needs of multiple teams, systems, and products. They must also be comfortable with supporting data conversions associated with system modernization and/or replacement efforts. The right candidate will be excited by the prospect of optimizing the Commonwealth's longitudinal data architecture to support our next generation of data initiatives.
Responsibilities:
- Create and maintain optimal data pipeline architecture.
- Design and implement cloud-based data solutions using AWS services such as S3, Glue, Redshift, EMR, Athena, Kinesis, and Lambda.
- Build and maintain data orchestration workflows using tools such as Apache Airflow, AWS Step Functions, or similar services.
- Assemble large, complex data sets that meet functional / non-functional business area requirements.
- Engineer analytical data environments that support advanced statistical analysis, predictive modeling, machine learning, and evidence-building activities.
- Partner with data scientists, research analysts, and policy analysts to ensure enterprise data assets support reproducible research and statistical modeling.
- Identify, design, and implement internal process improvements: e.g. automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability.
- Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and 'big data' technologies.
- Build analytics tools that utilize the data pipeline to provide actionable insights into education and workforce outcomes, operational efficiency, and other key business performance metrics.
- Develop and maintain data pipeline monitoring, alerting, and logging frameworks.
- Implement data quality checks and validation frameworks across pipelines.
- Develop dashboards, reports, analytical models, and/or other decision-support products.
- Work with stakeholders including the Executive, Data, Design, and Support teams to assist with data-related technical issues and support their data infrastructure needs.
- Ensure compliance with applicable Commonwealth policies, data governance standards, privacy requirements, security controls, and interagency data-sharing agreements.
- Create data tools for analytics and data scientist team members that assist them in building and optimizing data products needed to support ongoing operations and data-informed decision-making.
- Work with data and analytics professionals to strive for greater functionality in our data systems.
BASIC QUALIFICATIONS:
- Advanced SQL knowledge and proficiency working with relational databases, query authoring (SQL), as well as working familiarity with a variety of databases.
- Experience building and optimizing 'big data' data pipelines, architectures, and data sets.
- Advanced knowledge and proficiency designing, building, and refining dashboards, reports, analytical models, and decision-support products for executive and non-technical users.
- Experience working with student-level higher education data, including establishing linkages to other sources (e.g. employment and wage data) to measure outcomes and/or generate insights for decision-making.
- Experience performing root-cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement.
- Strong analytic skills related to working with unstructured datasets.
- Experience building processes supporting data transformation, data structures, metadata, dependency, and workload management.
- A successful history of manipulating, processing, and extracting value from large, disconnected datasets.
- Working knowledge of message queuing, stream processing, and highly scalable 'big data' data stores.
- Strong project management and organizational skills.
- Experience supporting and working with cross-functional teams in a dynamic environment.
SKILL - REQUIRED / DESIRED - AMOUNT OF EXPERIENCE
- Hands-on experience with cloud-based data services such as AWS (e.g. S3, EC2, Glue, EMR, RDS, Redshift, Athena, Lambda, DynamoDB) - Required - 5 Years
- Proficiency in object-oriented/object function scripting languages: e.g. Python, R, Java, C++, Scala - Required - 5 Years
- Proficiency in relational SQL and NoSQL databases, including Oracle, MS SQL Server, Postgres, Cassandra. - Required - 5 Years
- Experience with version control systems such as Git and CI/CD pipelines - Required - 5 Years
- Knowledge of data modeling techniques including dimensional modeling, data vault, and star/snowflake schemas. - Required - 5 Years
- Familiarity with data cataloging and metadata management tools such as AWS Glue Data Catalog. - Required - 5 Years
- Understanding of data governance, data lineage, and data quality frameworks. - Required - 5 Years
- Knowledge of REST API design and integration patterns. - Required - 5 Years
- Experience with big data tools: e.g. Hadoop, Spark, Kafka. - Required - 5 Years
- Experience with data pipeline and workflow management tools: e.g. Azkaban, Luigi, Airflow. - Required - 5 Years
- Experience with data integration services solutions from vendors such as Informatica, MuleSoft, Talend, and TIBCO. - Required - 5 Years
- Experience with stream-processing systems: e.g. Storm, Spark-Streaming. - Required - 5 Years
NOTES:
Work will be remote.
VIVA is an equal opportunity employer. All qualified applicants have an equal opportunity for placement, and all employees have an equal opportunity to develop on the job. This means that VIVA will not discriminate against any employee or qualified applicant on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status.
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Sign up for free to unlock all listings, filter by visa type, and get alerts for new Data Analytics Engineer roles.
Get Access To All JobsTips for Finding Visa Sponsorship as a Data Analytics Engineer
Target companies with a history of H-1B filings
Large tech companies, financial institutions, and healthcare analytics firms file H-1B petitions for data analytics engineers at high rates. Focusing your search on employers with consistent filing histories significantly improves your odds of securing sponsorship.
Emphasize your full data stack in applications
Employers sponsoring this role want candidates who can handle ingestion, transformation, and visualization end to end. Highlighting proficiency in tools like dbt, Spark, and Airflow signals the depth that justifies the time and cost of sponsorship.
Frame your degree in terms of specialty occupation
USCIS requires a specific degree nexus for H-1B approval. Explicitly connecting your degree in computer science, engineering, or statistics to the analytics engineering role strengthens the specialty occupation argument in your employer's petition.
Pursue roles at companies with cap-exempt status
Universities, nonprofit research organizations, and affiliated entities are exempt from the H-1B lottery cap. Data analytics engineers in academic medical centers or research institutions can receive H-1B approval at any point in the year without lottery risk.
Consider the O-1A if you have standout achievements
If you've published research, spoken at industry conferences, or led high-impact analytics projects, the O-1A visa may be a viable alternative. It bypasses the lottery entirely and is increasingly used by experienced data professionals with documented achievements.
Use Migrate Mate to filter for verified sponsoring employers
Not every job posting that sounds relevant will lead to sponsorship. Migrate Mate surfaces data analytics engineer roles from employers with confirmed sponsorship track records, saving you the time of applying to companies that won't support a visa petition.
Frequently Asked Questions
Does a data analytics engineer role qualify for H-1B sponsorship?
Yes, data analytics engineer is a well-established specialty occupation under H-1B visa criteria. USCIS expects a bachelor's degree or higher in computer science, statistics, information systems, or a closely related field. Roles requiring knowledge of data modeling, pipeline architecture, or statistical analysis typically satisfy the theoretical and practical application standard. Generic titles paired with vague job duties are more likely to face a request for evidence, so detailed job descriptions matter.
What degree do I need for an employer to sponsor me as a data analytics engineer?
A bachelor's degree in computer science, data science, statistics, mathematics, or information systems is the most defensible combination. Degrees in adjacent fields like economics or engineering can work if your coursework included quantitative methods and programming. USCIS evaluates whether the degree is in a specialty directly related to the role, so a business administration degree with no technical coursework is unlikely to qualify without additional credentials or a formal equivalency evaluation.
How competitive is H-1B sponsorship for data analytics engineers compared to other roles?
Data analytics engineer sits in a strong position. It's technical enough to have a clear degree requirement, which helps avoid RFEs, and demand from employers is high enough that many companies view sponsorship as a standard part of hiring for this title. The H-1B lottery still applies, so selection isn't guaranteed, but cap-exempt alternatives, OPT extension periods, and O-1A pathways mean there are multiple ways to stay authorized while pursuing a long-term visa.
Can I get sponsored as a data analytics engineer if my degree is in a non-technical field?
It's harder but not impossible. If your undergraduate degree is in a non-technical field, a master's in data science, computer science, or applied statistics can satisfy the specialty occupation requirement. Alternatively, USCIS allows three years of specialized work experience to substitute for one year of formal education, so significant hands-on experience building analytics infrastructure may compensate for a less technical degree when combined with relevant certifications.
Where can I find data analytics engineer jobs that offer visa sponsorship?
Migrate Mate is built specifically for this. It filters data analytics engineer roles by employers who have a documented history of filing H-1B or other work visa petitions, so you're not wasting applications on companies that won't sponsor. The platform is designed for international job seekers on OPT, H-1B, or other work authorization, and it focuses on roles where sponsorship is a realistic outcome rather than an afterthought.
What is the prevailing wage requirement for sponsored Data Analytics 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.