Sr. Data Engineer - Services Special Projects

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)