Principal Data Engineering Lead - Services Special Project

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. 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. Preferred Qualifications Experience with data versioning tools and frameworks (e.g., DVC, Delta Lake) Experience storing/serving embeddings (e.g., pgvector, Milvus, FAISS) 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. Pay & Benefits At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $262,500 and $394,000, and your base pay will depend on your skills, qualifications, experience, and location. Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.