Data Engineer Jobs at Apple with Visa Sponsorship
Apple's Data Engineer roles sit at the intersection of massive-scale infrastructure and consumer product development, covering data pipelines, analytics engineering, and platform work across hardware and services. Apple has a strong track record of sponsoring international talent for this function across multiple visa categories.
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Get Access To All JobsApple is where extraordinary people do their best work. If making a real impact excites you, a career here might be your dream - just be prepared to dream big.
Apple’s growing supply chain complexity demands innovative approaches beyond traditional data engineering. You’ll join a team designing and building modern, scalable data infrastructure that powers analytics, machine learning, and AI-driven decision-making across Operations. You’re passionate about building reliable data systems, staying ahead of technology trends, and thrive navigating ambiguity in a fast-paced environment. If this sounds like you, we’d love to talk.
Description
Engage with business and analytics teams to deeply understand data needs and translate requirements into robust, scalable engineering solutions that directly impact Operations decisions
Design and implement end-to-end data pipelines and architectures from ingestion and transformation to delivery across batch and real-time streaming workloads
Build and maintain high-quality data models (dimensional, relational, or knowledge graph-based) using modern transformation frameworks such as dbt, powering analytics and AIML use cases at scale
Architect and operate data workflows using orchestration tools (e.g., Apache Airflow, etc) with built-in monitoring, alerting, and SLA management
Implement data observability, lineage tracking, and validation frameworks to uphold data integrity and trustworthiness across the platform
Collaborate with Data Scientists, ML Engineers, Software Engineers and Analysts to operationalize models and ensure data infrastructure supports production AIML workflows
Partner with infrastructure and platform teams to manage cloud-native data environments (Snowflake, Spark, Delta Lake / Apache Iceberg) with a focus on performance, cost efficiency, and scalability
Leverage AI-assisted development tools (e.g., GitHub, Claude) and LLM-powered agents to accelerate pipeline authoring, code review, documentation, and transformation logic generation from natural language specifications
Apply DataOps principles including CI/CD pipelines, version control, automated testing, and containerization (Docker, Kubernetes) to deliver reliable, production-grade data products
Champion a data product mindset, enabling self-serve analytics and reducing bottlenecks for downstream consumers
Tune query performance, partitioning strategies, and storage optimization for data at scale in cloud warehouses and lakehouses
Develop and maintain clear technical documentation including data dictionaries, lineage diagrams, and architecture decision records
Present data infrastructure capabilities, health metrics, and architectural recommendations to senior leadership in clear, non-technical terms
Research and evaluate emerging data engineering technologies including streaming architectures, GenAI-powered data tooling, and next-generation warehousing to expand the team’s capabilities and accelerate innovation
Minimum Qualifications
MS in Computer Science, Data Engineering, Statistics, Applied Math, Data Science, Operations Research or a related field and 8+ years of industry experience OR BS in related field with 10+ years hands-on industry experience
Domain expertise in supply chain, operations management, logistics, planning & forecasting, production integration, channel management
Demonstrated expertise building and operating large-scale ETL/ELT pipelines using Python, SQL, and modern frameworks (dbt, Spark, Kafka/Flink for streaming)
Proficiency with cloud data platforms (e.g. Snowflake) and open table formats (Delta Lake, Apache Iceberg)
Strong command of advanced SQL for complex data modeling, query optimization, and analytics engineering
Experience with workflow orchestration tools (Apache Airflow or equivalent) and building production-grade, monitored pipelines
Hands-on experience implementing data quality frameworks, observability tooling, and data lineage tracking in production environments
Experienced with implementation and productionalization of GenAI and Agentic AI tooling including LLM-assisted code generation, MCP servers, and AI-powered data pipeline automation
Experience with data visualization and self-service analytics platforms (e.g., Tableau, Streamlit, ThoughtSpot) and the ability to build light front-end data products
Track record of staying current with industry best practices, rapidly adopting emerging technologies (e.g., vector databases, RAG pipelines, AI-native data tools), and building functional prototypes to validate concepts
Preferred Qualifications
Ability to work well in a fast-paced, iterative environment and deliver projects under timeline pressures
Champion a culture of experimentation and continuous learning, bringing innovative and strategic thinking to reporting, business analytics, and AI-powered automation
Exceptional ability to communicate complex data architecture decisions clearly to both technical peers and non-technical senior stakeholders
Strong interpersonal and collaboration skills to partner effectively across functions, share knowledge, and integrate diverse feedback
Self-sufficient with an ability to thrive in an environment of autonomy amidst ambiguity, with a high bias for action and meticulous attention to data integrity
Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant.
Apple is where extraordinary people do their best work. If making a real impact excites you, a career here might be your dream - just be prepared to dream big.
Apple’s growing supply chain complexity demands innovative approaches beyond traditional data engineering. You’ll join a team designing and building modern, scalable data infrastructure that powers analytics, machine learning, and AI-driven decision-making across Operations. You’re passionate about building reliable data systems, staying ahead of technology trends, and thrive navigating ambiguity in a fast-paced environment. If this sounds like you, we’d love to talk.
Description
Engage with business and analytics teams to deeply understand data needs and translate requirements into robust, scalable engineering solutions that directly impact Operations decisions
Design and implement end-to-end data pipelines and architectures from ingestion and transformation to delivery across batch and real-time streaming workloads
Build and maintain high-quality data models (dimensional, relational, or knowledge graph-based) using modern transformation frameworks such as dbt, powering analytics and AIML use cases at scale
Architect and operate data workflows using orchestration tools (e.g., Apache Airflow, etc) with built-in monitoring, alerting, and SLA management
Implement data observability, lineage tracking, and validation frameworks to uphold data integrity and trustworthiness across the platform
Collaborate with Data Scientists, ML Engineers, Software Engineers and Analysts to operationalize models and ensure data infrastructure supports production AIML workflows
Partner with infrastructure and platform teams to manage cloud-native data environments (Snowflake, Spark, Delta Lake / Apache Iceberg) with a focus on performance, cost efficiency, and scalability
Leverage AI-assisted development tools (e.g., GitHub, Claude) and LLM-powered agents to accelerate pipeline authoring, code review, documentation, and transformation logic generation from natural language specifications
Apply DataOps principles including CI/CD pipelines, version control, automated testing, and containerization (Docker, Kubernetes) to deliver reliable, production-grade data products
Champion a data product mindset, enabling self-serve analytics and reducing bottlenecks for downstream consumers
Tune query performance, partitioning strategies, and storage optimization for data at scale in cloud warehouses and lakehouses
Develop and maintain clear technical documentation including data dictionaries, lineage diagrams, and architecture decision records
Present data infrastructure capabilities, health metrics, and architectural recommendations to senior leadership in clear, non-technical terms
Research and evaluate emerging data engineering technologies including streaming architectures, GenAI-powered data tooling, and next-generation warehousing to expand the team’s capabilities and accelerate innovation
Minimum Qualifications
MS in Computer Science, Data Engineering, Statistics, Applied Math, Data Science, Operations Research or a related field and 8+ years of industry experience OR BS in related field with 10+ years hands-on industry experience
Domain expertise in supply chain, operations management, logistics, planning & forecasting, production integration, channel management
Demonstrated expertise building and operating large-scale ETL/ELT pipelines using Python, SQL, and modern frameworks (dbt, Spark, Kafka/Flink for streaming)
Proficiency with cloud data platforms (e.g. Snowflake) and open table formats (Delta Lake, Apache Iceberg)
Strong command of advanced SQL for complex data modeling, query optimization, and analytics engineering
Experience with workflow orchestration tools (Apache Airflow or equivalent) and building production-grade, monitored pipelines
Hands-on experience implementing data quality frameworks, observability tooling, and data lineage tracking in production environments
Experienced with implementation and productionalization of GenAI and Agentic AI tooling including LLM-assisted code generation, MCP servers, and AI-powered data pipeline automation
Experience with data visualization and self-service analytics platforms (e.g., Tableau, Streamlit, ThoughtSpot) and the ability to build light front-end data products
Track record of staying current with industry best practices, rapidly adopting emerging technologies (e.g., vector databases, RAG pipelines, AI-native data tools), and building functional prototypes to validate concepts
Preferred Qualifications
Ability to work well in a fast-paced, iterative environment and deliver projects under timeline pressures
Champion a culture of experimentation and continuous learning, bringing innovative and strategic thinking to reporting, business analytics, and AI-powered automation
Exceptional ability to communicate complex data architecture decisions clearly to both technical peers and non-technical senior stakeholders
Strong interpersonal and collaboration skills to partner effectively across functions, share knowledge, and integrate diverse feedback
Self-sufficient with an ability to thrive in an environment of autonomy amidst ambiguity, with a high bias for action and meticulous attention to data integrity
Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant.
See all 540+ Data Engineer at Apple jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Data Engineer at Apple roles.
Get Access To All JobsTips for Finding Data Engineer Jobs at Apple Jobs
Align your portfolio to Apple's data stack
Apple's Data Engineer roles consistently require experience with large-scale distributed systems and tools like Spark, Presto, and internal pipeline orchestration. Build or document projects that demonstrate you've worked with data at consumer-product scale, not just enterprise BI tooling.
Target postings that name your visa type
Apple's Data Engineer job descriptions often specify which work authorization types they'll support. Filter for roles that explicitly list your visa category, whether H-1B, E-3, or TN, so you're not eliminated at the recruiter screening stage before a conversation starts.
Understand Apple's legal team handles PERM internally
Apple manages Green Card sponsorship through PERM labor certification with its in-house immigration team. Knowing this means you can ask directly during offer negotiation whether the role is designated for employer-sponsored permanent residency, and at what seniority level that typically begins.
Use Migrate Mate to surface Apple's open roles
Apple posts Data Engineer openings across teams with varying sponsorship scopes. Use Migrate Mate to filter specifically for Apple roles that match your visa type, so you're spending time only on positions where sponsorship is already confirmed.
Request premium processing before your start date
If you're transferring an existing H-1B to Apple, USCIS premium processing gets a decision within 15 business days. Coordinate with Apple's immigration team early so the I-129 petition is filed with enough runway before your intended first day.
Validate your OPT STEM extension eligibility before accepting
Apple is an E-Verify participant, which is a requirement for F-1 students on STEM OPT extensions. Before signing an offer, confirm your degree field appears on the official STEM Designated Degree Program List so your 24-month extension remains valid from day one.
Data Engineer at Apple jobs are hiring across the US. Find yours.
Find Data Engineer at Apple JobsFrequently Asked Questions
Does Apple sponsor H-1B visas for Data Engineers?
Yes, Apple sponsors H-1B visas for Data Engineers and has done so consistently across teams in areas like machine learning infrastructure, analytics, and platform engineering. Sponsorship decisions are role-specific and handled by Apple's in-house immigration team. Because the H-1B is subject to an annual lottery, timing your application cycle and having your offer in place before the March registration window matters.
How do I apply for Data Engineer jobs at Apple?
Applications go through Apple's careers portal at jobs.apple.com. Search for Data Engineer roles and filter by location, typically Santa Clara Valley or Seattle. Tailoring your resume to highlight pipeline architecture, data modeling, and distributed systems experience improves your chances at the recruiter screen. You can also browse Apple's open Data Engineer roles filtered by visa type on Migrate Mate before applying directly.
Which visa types does Apple commonly sponsor for Data Engineer roles?
Apple sponsors H-1B, H-1B1 (for Chilean and Singaporean nationals), E-3 (for Australian nationals), and TN visas for qualifying Canadian and Mexican candidates. F-1 OPT and STEM OPT extensions are also supported for recent graduates. For longer-term pathways, Apple sponsors EB-2 and EB-3 Green Cards through the PERM labor certification process for eligible employees.
What qualifications does Apple expect for Data Engineer roles?
Apple's Data Engineer postings typically expect a bachelor's or master's degree in computer science, engineering, or a related technical field. Hands-on experience with distributed data processing frameworks like Spark or Flink, proficiency in SQL and Python, and familiarity with cloud infrastructure are standard requirements. Senior roles add expectations around data platform design and cross-functional stakeholder work with product and machine learning teams.
How do I navigate the timeline from offer to visa filing at Apple?
Once you have a signed offer, Apple's immigration team initiates the appropriate petition based on your visa category. For H-1B cap-subject cases, this process is tied to the annual USCIS registration window in March, with an October 1 start date at the earliest. For cap-exempt transfers or E-3 and TN filings, processing can move faster. Expect several weeks of internal preparation before any government filing begins.
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