Description
Summary
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.
Description
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.
Key Responsibilities
Build and implement batch and streaming ETL/ELT pipelines that ingest, process, and model data from diverse sources, including unstructured media and real-time event streams, ensuring high reliability, performance, and scalability.
Develop and maintain Kafka-based ingestion and processing pipelines, ensuring reliable data delivery across services and into the data lake.
Build robust logical and physical data models with a focus on dimensional modeling, versioning, and storage patterns (e.g., Parquet, ORC) optimized for ingest, reporting, and operational use cases.
Define and enforce data quality checks, SLAs, and observability standards to ensure data is accurate, timely, versioned, and trusted by stakeholders.
Integrate and enrich raw signals with metadata and attribution to power downstream use cases such as analytics, billing, planning, and optimization.
Implement standard methodologies for data lineage, metadata management, schema governance, versioning, and security in alignment with Apple's standards for data protection and privacy.
Deliver solutions that include logging, anomaly detection, data validation, cleaning, and transformation, with strong emphasis on monitoring, debuggability, and continuous improvement.
Work closely with ML engineers, data scientists, platform teams, and leadership to translate requirements into scalable, reliable data solutions.
Help advance the team's data stack, including tooling, frameworks, and standards for development, testing, deployment, and operations.
Align our team with other Apple teams strategically, participating in larger scale discussions and deliverables across our ecosystem.
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)
Apple