OPT Data Analytics Engineer Jobs
Data Analytics Engineer jobs are among the more OPT-friendly roles in tech, employers regularly sponsor H-1B visas for this position, and the role's direct tie to a quantitative degree field makes it a strong fit for OPT STEM extension eligibility, giving you up to three years of authorized work.
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Get Access To All JobsINTRODUCTION
Imagine what you could do here. At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your work, and there’s no telling what you could accomplish.
AI & Data Platforms (AiDP) is IS&T's engine for AI-powered innovation. The team brings together data, application development, and machine learning - including generative AI - along with data services and customer success functions, to help IS&T build solutions more efficiently and streamline the adoption and embedding of generative AI across Apple.
DESCRIPTION
The Developer Experience Platform team is building the next generation of AI-powered tools that accelerate how applications are developed across Apple. We are looking for a Data & Analytics Engineer to help design, build, and scale the data foundation that powers this platform.
In this role, you will develop robust data pipelines and analytics systems that enable AI agents, autonomous workflows, and data-driven insights - directly impacting how software is built at scale.
Responsibilities
As a hands-on engineer, you will:
- Design, build, and maintain scalable data pipelines and ELT workflows to support AI and analytics use cases
- Develop clean, reliable, and well-modeled datasets for both batch and real-time consumption
- Partner closely with AI/ML engineers and platform teams to deliver high-quality data for model training, inference, and agent workflows
- Implement data quality, observability, and monitoring systems to ensure trust and reliability across pipelines
- Build and optimize data models in modern cloud data warehouses (e.g., Snowflake, BigQuery, Databricks)
- Use tools like DBT to create modular, testable, and well-documented transformation layers
- Orchestrate and manage workflows using tools such as Airflow, Prefect, or Dagster
- Optimize pipelines and queries for performance, scalability, and cost efficiency
- Contribute to the design of the data architecture supporting AI agents and autonomous workflows
- Enable self-service analytics and reporting for engineering and product teams
- Collaborate across teams to define and implement best practices for data engineering in an AI-first platform
MINIMUM QUALIFICATIONS
- 3+ years of hands-on experience in data engineering, analytics engineering, or a related role in a production environment
- Proficiency in Python and SQL, including pipeline development, automation, and performance optimization
- Hands-on experience with cloud data warehouses (e.g., Snowflake, BigQuery, or Databricks)
- Experience implementing monitoring, logging, and observability for data pipelines
- Experience with data modeling
- B.S. in Computer Science or similar or equivalent industry experience
PREFERRED QUALIFICATIONS
- Experience building AI/LLM-powered data pipelines, including RAG systems and integrations with APIs such as OpenAI or Anthropic
- Experience with real-time/streaming data systems such as Apache Kafka, Flink, or Spark Structured Streaming
- Experience with workflow orchestration tools such as Airflow, Prefect, or Dagster
- Knowledge of MLOps workflows, including feature engineering, model deployment, and monitoring (e.g., MLflow, Vertex AI)
- Experience with data quality, governance, and lineage tools (e.g., Great Expectations, Monte Carlo)
- Experience building and maintaining ELT pipelines using DBT
- Experience building dashboards and analytics using tools like Tableau, Looker, or Power BI
- Working knowledge of cloud platforms (AWS, GCP, or Azure) and associated data services (e.g., S3, Glue, Dataflow)
INTRODUCTION
Imagine what you could do here. At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your work, and there’s no telling what you could accomplish.
AI & Data Platforms (AiDP) is IS&T's engine for AI-powered innovation. The team brings together data, application development, and machine learning - including generative AI - along with data services and customer success functions, to help IS&T build solutions more efficiently and streamline the adoption and embedding of generative AI across Apple.
DESCRIPTION
The Developer Experience Platform team is building the next generation of AI-powered tools that accelerate how applications are developed across Apple. We are looking for a Data & Analytics Engineer to help design, build, and scale the data foundation that powers this platform.
In this role, you will develop robust data pipelines and analytics systems that enable AI agents, autonomous workflows, and data-driven insights - directly impacting how software is built at scale.
Responsibilities
As a hands-on engineer, you will:
- Design, build, and maintain scalable data pipelines and ELT workflows to support AI and analytics use cases
- Develop clean, reliable, and well-modeled datasets for both batch and real-time consumption
- Partner closely with AI/ML engineers and platform teams to deliver high-quality data for model training, inference, and agent workflows
- Implement data quality, observability, and monitoring systems to ensure trust and reliability across pipelines
- Build and optimize data models in modern cloud data warehouses (e.g., Snowflake, BigQuery, Databricks)
- Use tools like DBT to create modular, testable, and well-documented transformation layers
- Orchestrate and manage workflows using tools such as Airflow, Prefect, or Dagster
- Optimize pipelines and queries for performance, scalability, and cost efficiency
- Contribute to the design of the data architecture supporting AI agents and autonomous workflows
- Enable self-service analytics and reporting for engineering and product teams
- Collaborate across teams to define and implement best practices for data engineering in an AI-first platform
MINIMUM QUALIFICATIONS
- 3+ years of hands-on experience in data engineering, analytics engineering, or a related role in a production environment
- Proficiency in Python and SQL, including pipeline development, automation, and performance optimization
- Hands-on experience with cloud data warehouses (e.g., Snowflake, BigQuery, or Databricks)
- Experience implementing monitoring, logging, and observability for data pipelines
- Experience with data modeling
- B.S. in Computer Science or similar or equivalent industry experience
PREFERRED QUALIFICATIONS
- Experience building AI/LLM-powered data pipelines, including RAG systems and integrations with APIs such as OpenAI or Anthropic
- Experience with real-time/streaming data systems such as Apache Kafka, Flink, or Spark Structured Streaming
- Experience with workflow orchestration tools such as Airflow, Prefect, or Dagster
- Knowledge of MLOps workflows, including feature engineering, model deployment, and monitoring (e.g., MLflow, Vertex AI)
- Experience with data quality, governance, and lineage tools (e.g., Great Expectations, Monte Carlo)
- Experience building and maintaining ELT pipelines using DBT
- Experience building dashboards and analytics using tools like Tableau, Looker, or Power BI
- Working knowledge of cloud platforms (AWS, GCP, or Azure) and associated data services (e.g., S3, Glue, Dataflow)
See all 3,651+ OPT Data Analytics Engineer Jobs
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Get Access To All JobsTips for Finding OPT Sponsorship as a Data Analytics Engineer
Lead with your tech stack, not your visa status
Employers screening for Data Analytics Engineers look for SQL, dbt, Spark, and cloud platforms first. Front-loading your technical skills in your resume and outreach shifts the conversation to your value before visa logistics come up.
Target companies with established data engineering teams
Organizations with dedicated data infrastructure teams already have immigration counsel and H-1B sponsorship processes in place. Smaller companies hiring their first analytics engineer often lack the legal infrastructure to sponsor efficiently or at all.
Document the degree-to-role connection clearly
USCIS scrutinizes OPT STEM extension eligibility. Keeping a record of how your daily work, building pipelines, modeling data, running analyses, directly applies your degree in computer science, statistics, or engineering strengthens your DSO reporting and any future H-1B petition.
Apply before your OPT start date, not after
Most Data Analytics Engineer roles take four to eight weeks to move from application to offer. Starting your search three to four months before your OPT period begins gives you time to clear interviews without racing against your authorization window.
Highlight end-to-end project ownership in interviews
Interviewers for this role want to see that you can own a data product from ingestion to reporting. Describing projects where you designed the pipeline, modeled the schema, and delivered the dashboard signals seniority that makes sponsorship investment easier to justify.
Ask about sponsorship timeline, not just willingness
Some employers are willing to sponsor but have never done it, which creates delays. Asking whether the company has sponsored H-1B visas before and whether they work with outside immigration counsel tells you whether sponsorship is realistic within your OPT window.
Data Analytics Engineer OPT: Frequently Asked Questions
Does a Data Analytics Engineer role qualify for OPT STEM extension?
It depends on your degree field, not just the job title. If your degree is in computer science, information systems, statistics, mathematics, or a related STEM field listed on the DHS STEM Designated Degree Program List, and your day-to-day work directly applies that degree, the role typically qualifies for the 24-month STEM OPT extension. Your DSO makes the final determination.
How do I find Data Analytics Engineer jobs that sponsor OPT and H-1B visas?
Migrate Mate filters job listings specifically for roles where employers are open to sponsoring F-1 OPT and H-1B candidates, which saves significant time compared to applying broadly and discovering sponsorship limitations late in the process. Searching by role and filtering for visa sponsorship on Migrate Mate surfaces employers who have already indicated willingness to sponsor work authorization.
Can I work as a Data Analytics Engineer on OPT before my H-1B is approved?
Yes. OPT authorizes you to work full-time in a role directly related to your degree field. If your employer files an H-1B petition before your OPT expires and you have a timely filed extension or cap-gap coverage, you can continue working through the transition. Your employment authorization does not pause between OPT and H-1B approval.
What degree fields are most relevant for a Data Analytics Engineer OPT position?
Computer science, data science, information systems, statistics, applied mathematics, and industrial engineering are the most common degree fields that align with this role. USCIS evaluates both the job duties and the degree field when assessing H-1B specialty occupation, so roles requiring advanced SQL, distributed systems, or statistical modeling are easier to support with engineering and quantitative degrees.
Do I need to report a Data Analytics Engineer job to my DSO while on OPT?
Yes. All OPT employment must be reported to your DSO within 10 days of starting. You need to provide your employer's name, address, and a brief description of how the role relates to your degree. For STEM OPT extension, your employer must also be enrolled in E-Verify, and you and your employer need to complete a training plan on Form I-983.