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Apple

Sr Data Engineer, AIML

Cupertino, CA$184,700 – $324,800Posted Oct 8, 2026

Apply on company site View full job

Why you should apply for a job to Apple:

Ranked as one of the Best Technology Companies in 2020
3.7/5 in overall job satisfaction
3.1/5 in supportive management
66% say women are treated fairly and equally to men
65% would recommend this company to other women
66% say the CEO supports gender diversity

Ratings are based on anonymous reviews by Fairygodboss members. See all reviews ›

Are you excited to tackle some of the most ambitious technical challenges in Apple Intelligence? Be involved in collaborating closely with our machine learning researchers, engineers, and data scientists? Together, you will orchestrate groundbreaking research initiatives and develop transformative products designed to build a significant impact for billions of users worldwide!

The AI and Machine Learning team is looking for Senior Data Engineer to build world-class data infrastructure and solutions that are used by data scientists, ML engineers and researchers to power Apple Foundation Model lifecycle.

Description

We are looking for a skilled Senior Data Engineer to join our engineering team. In this role, you will be responsible for designing, building, and maintaining scalable data pipelines, data platforms, and data integration solutions that enable reliable analytics, machine learning, and business intelligence capabilities across the organization.

","responsibilities":"Design, build, and optimize large-scale batch and streaming data pipelines using Google Cloud Dataflow (Apache Beam) and related technologies.

Develop and maintain data models and transformations in BigQuery to support analytics, reporting, and ML use cases.

Build scalable and reliable data architectures using GCP services such as Pub/Sub, Dataflow, BigQuery, Cloud Storage, Dataproc, and Composer.

Integrate data from multiple internal and external systems while ensuring data quality, consistency, lineage, and governance.

Collaborate closely with data scientists, ML engineers, analysts, and product teams to deliver production-grade data solutions.

Develop robust ETL/ELT workflows and orchestration pipelines using cloud-native tooling.

Monitor, troubleshoot, and optimize data pipelines for performance, cost efficiency, and reliability.

Implement best practices for data security, access control, and compliance in cloud environments.

Preferred Qualifications

7+ years of experience in data engineering or large-scale distributed systems.

Experience designing end-to-end data platforms or data mesh architectures.

Strong understanding of data governance, data lineage, and metadata management frameworks.

Experience supporting ML pipelines and MLOps workflows, including feature engineering and training data generation.

Experience building systems with strong reliability, observability, and SLAs (monitoring, alerting, debugging distributed pipelines).

Familiarity with Generative AI / LLM-based systems, including: LLM-powered data workflows, Agentic pipelines, Embedding/vector-based retrieval systems.

Experience influencing architecture decisions across teams and driving technical direction.

Experience collaborating in cross-functional, fast-paced tech environments or cloud-native organizations with a focus on building reliable, maintainable, and production-grade data systems.

Minimum Qualifications

Strong software engineering foundation with minimum 10+ years of experience in data engineering, backend engineering, or distributed systems.

Strong hands-on experience designing and building large-scale batch and streaming data pipelines using Python and distributed systems.

Expertise in ETL/ELT development, event-driven architectures, and working with high-volume datasets using technologies such as Pub/Sub, Dataflow (Apache Beam), and Spark-based processing (Dataproc or equivalent).

Deep expertise in the Google Cloud Platform data ecosystem, including BigQuery, Dataflow, Pub/Sub, and Composer (Airflow). Proven ability to design, optimize, and operate scalable data platforms, with strong experience in BigQuery performance tuning, cost optimization, data modeling, partitioning, clustering, and query optimization at scale.

Proficient in Python, version control, CI/CD practices, testing, and code reviews.

Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field (or equivalent practical experience).

Pay & Benefits

At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $184,700 and $324,800, and your base pay will depend on your skills, qualifications, experience, and location.

Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits

Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

About Apple

Working at Apple means doing more than you ever thought possible and having more impact than you ever imagined.

Size: 10,000+ employees
Industry: Technology: Manufacturing

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