Sr. Data Engineer - Services Special Projects

Apple • Cupertino, CA

2d

About The Position

Apple 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. Enrichment with language and embedding models supports critical experiences for Apple customers across multiple downstream applications.

The Data Engineer will design, build, and operate the infrastructure, applying expertise in ETL/ELT, data architecture, and applied ML pipelines. The role is responsible for creating massively scalable pipelines that turn raw data into a trusted foundation and support critical decision-making and operations across the system.

Requirements

• Master's 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 SQL and NoSQL databases, including Postgres, Cassandra, and Redis.
• Strong experience with distributed data processing frameworks, including Apache Spark.
• Strong experience with parallel processing frameworks such as BigTable and Hadoop.
• Strong software engineering fundamentals and proven experience with Scala and Java.
• Hands-on experience with Apache Kafka, Iceberg, and Flink.
• Experience with workflow orchestration tools, including Apache Airflow and Beam.
• Experience with AWS services such as S3, EMR, Lambda, Glue, Redshift, BigQuery, and Kinesis, or similar services.
• Experience with analytics frameworks including Trino, Presto, BigQuery, and Snowflake.
• Hands-on experience with big data lake architectures.
• Experience with containerization and orchestration, including Docker and 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, including structured and image data, 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 production, including inference runtimes or compilers such as ONNX Runtime and TensorRT/TensorRT-LLM, and serving frameworks such as Triton, vLLM, and 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.

Nice To Haves

• Experience with data versioning tools and frameworks such as DVC and Delta Lake.
• Excellent communication skills and a collaborative mindset.
• Experience storing and serving embeddings, such as with pgvector, Milvus, and FAISS.

Responsibilities

• Design, build, and operate infrastructure for ETL/ELT, data architecture, and applied ML pipelines.
• Create massively scalable pipelines that transform raw data into a trusted foundation.
• Build pipelines that process multimodal data, including structured and image data, and integrate ML model inference for data enrichment and transformation.
• Deploy, serve, and optimize LLMs or ML models in production for inference within data pipelines.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior