Principal Data Architect and Manager - Service Special Projects

Principal Data Architect and Manager - Service Special Projects

Cupertino, California, United States Software and Services

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.

• 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.
• 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…