Data Engineer

DESCRIPTION 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. 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) J-18808-Ljbffr