Principal Data Engineering Lead - Services Special Project

Apple•Cupertino, CA

13d

About The Position

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. We are seeking a Principal Data Engineer to lead and drive not only our team's data processing systems, but also to partner at a larger scale, coordinating and synching strategically with other business groups and organizations within Apple. We are seeking a Principal Data Engineering Lead with deep expertise in ETL/ELT, data architecture, and applied ML pipelines to drive the design, build, and operations of this infrastructure. As a key member of our team, you will be responsible for driving critical decisions and operations across the entire system while aligning strategically across Apple.

Requirements

Masters Degree
12+ 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
Proven experience delivering a consumer-oriented solution by participating at every stage of the development life-cycle.
Excellent communication skills and a collaborative mindset with past experience presenting and partnering with VP and C level decision makers.

Nice To Haves

Experience with data versioning tools and frameworks (e.g., DVC, Delta Lake)
Experience storing/serving embeddings (e.g., pgvector, Milvus, FAISS)

Responsibilities

Drive the design, build, and operations of data processing systems.
Partner at a larger scale, coordinating and synching strategically with other business groups and organizations within Apple.
Drive critical decisions and operations across the entire system while aligning strategically across Apple.
Build and maintain large-scale ETL/ELT data pipelines.
Design schemas optimized for analytics and reporting.
Build pipelines that process multimodal data (structured and image) and integrate ML model inference - including LLMs and embedding models - for data enrichment and transformation.
Deploy, serve, and optimize LLMs or ML models directly in the production, inference runtimes/compilers (ONNX Runtime, TensorRT/TensorRT-LLM), and serving frameworks (Triton, vLLM, TorchServe or similar).
Tune batching, KV-cache, and GPU utilization for low-latency, high-throughput real-time inference in a data pipeline.
Deliver a consumer-oriented solution by participating at every stage of the development life-cycle.
Present and partner with VP and C level decision makers.