Senior Data Engineer

As a Data Engineer on the Capacity Engineering team, you will help design, build, and operate the data foundation that drives capacity, cost, and power-related decisions across Apple's infrastructure footprint.\\n\\nIn this role, you will:\\n\\nArchitect, implement, and maintain large-scale batch and streaming pipelines that ingest, process, and model infrastructure telemetry, cost, metering, utilization, forecasting, and power metrics from multiple clouds and bare metal environments.\\n\\nDesign and evolve robust data models (with a strong focus on dimensional modeling) and storage patterns that support analytics, internal billing, and efficiency use-cases.\\n\\nTreat data as a product: define quality checks, SLAs, and observability to ensure data is accurate, timely, and trusted by stakeholders across Apple.\\n\\nIntegrate and enrich raw signals with metadata and attribution to power use cases such as internal billing/showback, usage understanding, efficiency and optimization, clawbacks, planning, and procurement.\\n\\nCollaborate closely with data scientists, software engineers, platform teams, finance partners, program managers, and leadership to translate requirements into scalable, reliable data solutions and services.\\n\\nImplement standard methodologies for data governance, lineage, metadata management, and security, in alignment with Apple's standards for data protection and privacy.\\n\\nBuild end-to-end data solutions that include logging, anomaly detection, data validation, cleaning, and transformation, with strong emphasis on monitoring, debuggability, and continuous improvement.\\n\\nContribute to the evolution of our data and platform stack, including tooling, frameworks, and standards for development, testing, deployment, and operations (CI/CD, infrastructure as code, etc.).

Apple's Capacity data engineering team, within the Apple Services Engineering organization, is building the centralized data backbone that powers how Apple understands, plans, and optimizes its cloud and data center infrastructure.\n\nWe engineer a unified, trusted data lake that consolidates cost, metering, utilization, forecasting, and power metrics produced by Apple platforms and systems (including bare metal) across both third-party and Apple internal clouds. Enriched with metadata and attribution, this becomes the single source of truth for internal billing, understanding usage and utilization, clawbacks, planning, procurement, and efficiency initiatives.\n\nWe collaborate with platform engineering, finance, capacity engineering, and leadership teams to build large-scale data pipelines, enable descriptive and predictive analytics, and power dashboards and products that support critical business decisions. This is your opportunity to help design and operate highly visible, global-scale systems processing petabytes of data and supporting hundreds of users across Apple.\n\nCome join us to help deliver the next generation of infrastructure insights at Apple.

Bachelors degree or equivalent experience in Computer Science, Information systems, Software Engineering, Data Science or related field.\n5+ years of experience in data engineering (or equivalent practical experience), including:\nBuilding and maintaining large-scale ETL/ELT data pipelines\nDistributed computing (e.g., Spark / PySpark) for data processing and automation\nQuery performance optimization and tuning at scale\nHands-on experience with:\nApache Spark and Airflow (or similar workflow/orchestration tools) for efficient large-scale data pipelines\nData modeling, especially dimensional modeling, and designing schemas optimized for analytics and reporting\nBig data platforms and/or data lake architectures\nExperience with CI/CD tooling such as Jenkins (or similar tools)\nExperience with cloud technologies, AWS/Google Cloud Platform (e.g., S3, EMR, Lambda, Glue, RDS/Redshift, Big-query, Kinesis or similar services)

Advanced degree in a related field a plus.\nExperience with data visualization / BI tools, such as Superset or Tableau (other tools like QuickSight, QlikView, Cognos, or Business Objects are a plus)\nExperience with containerization and orchestration, such as Docker and Kubernetes/EKS is a plus\nUnderstanding of authentication and authorization (AuthN/AuthZ) patterns\nKnowledge of data governance principles, data security best practices, and data privacy regulations