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Principal Data Architect and Manager - Service Special Projects
AppleCupertino, California, United StatesSenior$237,600 - $401,70012+ years
Apply We're building the large-scale data foundation that powers private, personalized experiences across Apple platforms. Our team designs and operates the systems that ingest, unify, and understand information at massive scale — turning petabytes of data from many sources into a single, high-quality, richly structured representation. This foundation is what intelligent search and on-device experiences rely on, and we build it with an uncompromising bar for data quality, freshness, and privacy.
We are looking for a Principal Data Architect and Manager to serve as both the senior technical authority and the people leader for our data platform.
As the Principal Data Architect and Manager on our team, you will serve as both the senior technical authority and the people leader for our data platform. You'll define and own the end-to-end architecture of a real-time, petabyte-scale data backbone: from ingestion through a multi-layered lakehouse to normalized serving layers that power downstream search, ranking, and on-device experiences. You'll also build, grow, and lead the team of data engineers who bring that architecture to life.
This is a hands-on principal role with multiple facets: you set the technical vision, personally shape the hardest architectural decisions, drive the roadmap through to production, and manage, mentor, and grow the engineers executing against it. Your leverage comes equally from what you design and from the team you build.
- 1. Architecture & Design (Architect scope)
- Define the end-to-end architecture of a multi-layered lakehouse on cloud object storage as the canonical layer — partitioning strategy, columnar formats (Parquet), open table formats (Iceberg or Delta), compaction, and cost management at petabyte scale.
- Establish the data governance framework: schema registries, lineage, metadata management, quality checkpoints, and access controls spanning the full lifecycle from raw ingestion to normalized serving layers, aligned with Apple's privacy and security standards.
- Architect batch, micro-batch, and streaming ETL/ELT pipelines capable of handling structured, semi-structured, and unstructured multimodal data, including image and other media, with real-time metadata extraction, schema augmentation, and enrichment.
- Design, build, and operate a fault-tolerant Apache Kafka streaming backbone, including topic design, schema evolution, consumer-group topology, and delivery-semantics guarantees across services.
- Set the architectural direction for entity resolution, conflation, and knowledge-graph construction at the scale of billions of frequently updated entities.
- Treat privacy as an architectural constraint, not a compliance step: data minimization, retention and deletion enforcement, and data privacy constraints designed into the platform from the first layer.
- 2. Technical Leadership & Implementation (Lead scope)
- Set the technical roadmap for the data platform and drive the team's execution against it, from architectural vision through to production delivery.
- Build the ingestion services that reliably land massive, heterogeneous streams from various partners, and own the data contracts with those producers.
- Lead the implementation of complex data transformations — normalization, augmentation, enrichment — with a strong bar for correctness, consistency, and analytical readiness.
- Continuously optimize pipeline performance, reliability, and cost, evaluating trade-offs between batch, micro-batch, and pure streaming models.
- Define SLAs, quality metrics, and observability standards that make the platform trusted by every downstream consumer.
- Represent the data platform in cross-team architectural forums, partnering closely with ML, search & ranking, on-device experience, and platform teams.
- 3. Team Leadership & Management (People scope)
- Partner with recruiting to attract, evaluate, and hire senior and staff data engineers; raise the technical bar with every hire.
- Manage a group of data engineers directly, own their performance, career development, and technical growth; mentor across levels on cloud-native design, distributed computing, and stream processing.
- Allocate work against the roadmap, unblock execution, drive design reviews, and hold a high bar for engineering craft and operational excellence.
- Communicate progress, trade-offs, and risks to senior leadership and to partner orgs; advocate for the investments the platform needs.
- Cultivate a healthy engineering culture: high ownership, strong review practices, thoughtful on-call, and a deep commitment to user privacy.
- MS Degree in Computer Science or related degree and 12+ years of experience in Data Architecture, Data Engineering, or Platform Engineering, with at least 5 years operating in a Principal, Staff, or Lead Manager capacity.
- Proven experience leading and managing engineers including hiring, performance management, and technical mentorship of senior ICs and managers.
- Track record of shipping petabyte-scale, low-latency data platforms in production and operating them under real-world load.
- Deep cloud expertise: expert-level proficiency with cloud object storage (e.g., AWS S3) and its architectural nuances for massive data lakes and lake-houses.
- Experience architecting systems for entity resolution, conflation, or knowledge-graph construction at scale — ideally involving billions of frequently updated entities.
- Experience designing pipelines that process multimodal data (structured, text, image) and integrate ML model inference including LLMs and embedding models: for enrichment and transformation.
- Familiarity with LLM/model-serving infrastructure trade-offs (inference runtimes, GPU-backed serving) to inform architectural decisions
- Streaming expertise: deep, hands-on knowledge of Apache Kafka (or comparable brokers like Kinesis) and complex stream processing (Spark Structured Streaming, Flink, or similar).
- Data modeling: exceptional ability to design logical and physical data models for large-scale ingest, retrieval, and analytical consumption — including dimensional modeling and lakehouse patterns.
- Experience defining SLAs, quality metrics, and observability standards for large-scale data platforms, with hands-on use of monitoring/alerting tooling (e.g., Prometheus/Grafana, Datadog, or OpenTelemetry-based tracing).
- Programming: command of at least one modern data-pipeline language (Scala, Java, or Python) and strong software engineering fundamentals.
- Cloud services integration: proven experience wiring together event notifications, queuing, orchestration, and compute services into resilient production pipelines.
- Experience with vector search technologies (e.g., Pinecone, Milvus) and storing/serving embeddings (e.g., pgvector, Milvus, FAISS)
- Excellent written and verbal communication; proven ability to align engineers, partner teams, and senior leadership from multiple lines of business around a shared technical direction, with experience bringing a consumer-oriented product from inception to production.
- Experience with embedding storage and retrieval (e.g., pgvector, Milvus, FAISS) and with graph databases (e.g., TigerGraph, Neo4j).
- 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
- Experience with data governance tools (e.g., Apache Atlas, AWS Glue Catalog, DataHub).
- Familiarity with Infrastructure as Code (Terraform, Pulumi) and modern CI/CD practice.
- Experience designing systems that handle petabytes of unstructured media data.
- Working knowledge of data privacy regulations and best practices for incorporating safety and compliance, and a demonstrated instinct for building privacy-preserving systems.