Principal Data Engineering Lead - Services Special Project

Summary

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.

Description

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.

Minimum Qualifications

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.

Preferred Qualifications

Experience with data versioning tools and frameworks (e.g., DVC, Delta Lake)

Experience storing/serving embeddings (e.g., pgvector, Milvus, FAISS)