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)