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.
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)