STEM OPT Data Analytics Engineer Jobs
Data Analytics Engineer roles sit squarely within STEM OPT eligibility, letting you work for up to 36 months post-graduation when your employer is enrolled in E-Verify. Your STEM degree in computer science, statistics, or a related field unlocks the 24-month extension, giving you a realistic runway to transition to long-term sponsorship.
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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)
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Get Access To All JobsTips for Finding STEM OPT Authorization as a Data Analytics Engineer
Verify your CIP code before applying
Check that your degree's Classification of Instructional Programs code maps to an approved STEM field on the official STEM OPT designated degree program list. Data science, computer science, and statistics CIP codes all qualify, but some interdisciplinary programs do not.
Confirm E-Verify enrollment before accepting offers
Ask the recruiter for the company's E-Verify employer identification number before signing anything. An employer that isn't enrolled can't legally employ you on STEM OPT, and switching roles mid-extension restarts your I-983 training plan clock.
Target employers with active H-1B filing history
Search Migrate Mate to filter Data Analytics Engineer roles by employers who have filed H-1B petitions, giving you a clear signal they're set up for long-term sponsorship before you invest time in their interview process.
Build your I-983 training plan around analytics deliverables
Draft measurable learning objectives tied to specific tools and outcomes, such as building a production data pipeline or completing an A/B testing framework. USCIS reviewers expect concrete, role-specific goals, not generic descriptions of analytics work.
Benchmark your offer against DOL prevailing wages
Use the OFLC Wage Search to look up the Level I through Level IV wage for Data Analytics Engineer roles in your target metro. Accepting below the applicable prevailing wage can jeopardize your employer's ability to file an LCA if they sponsor you for H-1B later.
Understand cap-gap protection if H-1B is filed
If your employer files an H-1B petition before your OPT expires and you're selected in the lottery, cap-gap automatically extends your work authorization through September 30. You can keep working your Data Analytics Engineer role uninterrupted while USCIS adjudicates the petition.
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Find Data Analytics Engineer JobsFrequently Asked Questions
Does a Data Analytics Engineer role qualify for the STEM OPT extension?
Yes, as long as your degree is in an approved STEM field such as computer science, statistics, mathematics, or information systems. The role itself must also be directly related to that degree. Check the O*NET profile for Data Analytics Engineer to confirm the educational alignment USCIS expects, and verify your program's CIP code against the official STEM designated degree list before filing.
How do I confirm an employer is enrolled in E-Verify before I start?
Ask the recruiter or HR contact for the company's E-Verify employer identification number during the offer stage, not after you've signed. You can also search the public E-Verify employer search tool by company name to confirm current enrollment status. If the employer isn't enrolled, they cannot legally employ you on STEM OPT, and your DSO cannot authorize the extension.
What should my I-983 training plan include for a Data Analytics Engineer position?
Your I-983 must list specific, measurable goals tied directly to your STEM degree and the Data Analytics Engineer role. Concrete examples include building and maintaining data pipelines, developing predictive models, or leading A/B test analyses. Vague descriptions of general analytics duties are the most common reason DSOs send training plans back for revision, so tie each goal to a tool, methodology, or deliverable.
Can cap-gap protect my work authorization if my employer sponsors me for H-1B?
Yes. If your employer files an H-1B petition before your OPT EAD expires and USCIS selects you in the lottery, cap-gap automatically extends your work authorization through September 30 of that fiscal year. You can continue working as a Data Analytics Engineer without interruption during the adjudication period. Your DSO will issue an updated I-20 reflecting the cap-gap extension once the petition is accepted.
Where can I find Data Analytics Engineer jobs with E-Verify employers?
Migrate Mate filters Data Analytics Engineer roles specifically for STEM OPT students, showing only employers already enrolled in E-Verify. You can also see each employer's H-1B filing history directly in the listing, so you know before applying whether a company has a track record of long-term sponsorship rather than discovering that gap after an offer.
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