At Apple, great ideas have a way of becoming phenomenal products, services, and customer experiences very quickly. Our team is building a massive, real-time platform that transforms continuous streams of multimodal data (including structured, image, and log data) into an intelligent, searchable foundation.
By enriching this data with language and embedding models, we power critical experiences for billions of Apple customers across multiple downstream applications.
Description
We are seeking an experienced Data Engineer with deep expertise in ETL/ELT, data architecture, and applied ML pipelines to design, build, and operate this infrastructure. As a key member of the team, you will be responsible for creating the massively scalable pipelines that turn raw data into a trusted foundation, driving critical decision-making and operations across the entire system.
Minimum qualifications
Masters Degree
10+ years of experience in data engineering, including building and maintaining large-scale ETL/ELT data pipelines
Proficiency in data modeling, especially dimensional modeling, and designing schemas optimized for analytics and reporting
Experience with leveraging databases including SQL/NoSQL Databases (including Postgres / Cassandra / Redis)
Strong experience with distributed data processing frameworks including Apache Spark
Strong experience with Parallel processing frameworks: BigTable/Hadoop
Strong software engineering fundamentals and proven experience with Scala, Java
Hands-on experience with Apache Kafka, Iceberg, and Flink.
Experience with workflow orchestration tools including Apache Airflow and Beam
Experience with AWS: e.g., S3, EMR, Lambda, Glue, Redshift, BigQuery, Kinesis, or similar services
Experience with Analytics frameworks including Trino (Presto, BigQuery, Snowflake)
Hands-on experience with big data lake architectures
Experience with containerization and orchestration (Docker, Kubernetes/EKS) and CI/CD tooling including Jenkins
Experience in Python and PySpark
Familiarity with graph databases such as TigerGraph
Experience building pipelines that process multimodal data (structured and image) and integrate ML model inference - including LLMs and embedding models - for data enrichment and transformation
Hands-on experience deploying, serving, and optimizing LLMs or ML models directly in the production, inference runtimes/compilers (ONNX Runtime, TensorRT/TensorRT-LLM), and serving frameworks (Triton, vLLM, TorchServe or similar).
Experience tuning batching, KV-cache, and GPU utilization for low-latency, high-throughput real-time inference in a data pipeline
Knowledge of data governance principles, data security best practices, and data privacy regulations
Preferred qualifications
Experience with data versioning tools and frameworks (e.g., DVC, Delta Lake)
Excellent communication skills and a collaborative mindset
Experience storing/serving embeddings (e.g., pgvector, Milvus, FAISS)
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.