Data & Analytics Engineer, AiDP

Imagine what you could do here. At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your work, and there's no telling what you could accomplish.

AI & Data Platforms (AiDP) is IS&T's engine for AI-powered innovation. The team brings together data, application development, and machine learning - including generative AI - along with data services and customer success functions, to help IS&T build solutions more efficiently and streamline the adoption and embedding of generative AI across Apple.

Description

The Developer Experience Platform team is building the next generation of AI-powered tools that accelerate how applications are developed across Apple. We are looking for a Data & Analytics Engineer to help design, build, and scale the data foundation that powers this platform.

In this role, you will develop robust data pipelines and analytics systems that enable AI agents, autonomous workflows, and data-driven insights-directly impacting how software is built at scale.","responsibilities":"As a hands-on engineer, you will:

Design, build, and maintain scalable data pipelines and ELT workflows to support AI and analytics use cases

Develop clean, reliable, and well-modeled datasets for both batch and real-time consumption

Partner closely with AI/ML engineers and platform teams to deliver high-quality data for model training, inference, and agent workflows

Implement data quality, observability, and monitoring systems to ensure trust and reliability across pipelines

Build and optimize data models in modern cloud data warehouses (e.g., Snowflake, BigQuery, Databricks)

Use tools like DBT to create modular, testable, and well-documented transformation layers

Orchestrate and manage workflows using tools such as Airflow, Prefect, or Dagster

Optimize pipelines and queries for performance, scalability, and cost efficiency

Contribute to the design of the data architecture supporting AI agents and autonomous workflows

Enable self-service analytics and reporting for engineering and product teams

Collaborate across teams to define and implement best practices for data engineering in an AI-first platform

Preferred Qualifications

Experience building AI/LLM-powered data pipelines, including RAG systems and integrations with APIs such as OpenAI or Anthropic

Experience with real-time/streaming data systems such as Apache Kafka, Flink, or Spark Structured Streaming

Experience with workflow orchestration tools such as Airflow, Prefect, or Dagster

Knowledge of MLOps workflows, including feature engineering, model deployment, and monitoring (e.g., MLflow, Vertex AI)

Experience with data quality, governance, and lineage tools (e.g., Great Expectations, Monte Carlo)