Full Stack Engineer - Python

Summary

Do you want to help build some of the largest and most consequential enterprise and customer technology systems in the world? Join Apple’s Information Systems and Technology (IS&T) organization. IS&T is the engine behind everything Apple does for customers and for the people who build for them. It’s Apple’s central nervous system. Supporting 2.5 billion active Apple devices, processing billions of secure transactions, and keeping the technology that defines modern life running flawlessly, IS&T makes the impossible feel effortless.Do you love building solutions to handle global complexity and immense scale? Imagine what you could do here. 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.

Minimum Qualifications

3+ years of professional software engineering or data application development experience. Strong programming skills in Python with good understanding of object-oriented and functional programming concepts. Experience developing production APIs or backend applications using FastAPI, Flask, Django, or comparable Python frameworks. Strong understanding of REST APIs, HTTP fundamentals, API contracts, error handling, authentication, and service integration. Experience developing modern web applications using React and JavaScript/TypeScript. Experience with Node.js and modern frontend/backend development tooling. Strong SQL skills and practical experience working with relational or analytical databases. Experience with databases/data platforms such as Snowflake, Oracle, PostgreSQL, SingleStore, or similar technologies. Understanding of software engineering fundamentals including data structures, algorithms, object-oriented design, API design, testing, debugging, and version control. Experience writing automated unit and integration tests. Good understanding of distributed application fundamentals including reliability, scalability, availability, latency, security, and observability. Experience using Git and participating in collaborative development and code-review workflows. Ability to independently investigate problems and deliver solutions with limited supervision. Strong communication skills and ability to work with both technical teams and business stakeholders.

Preferred Qualifications

Experience building reporting, analytics, financial, accounting, payments, commerce, or enterprise applications. Understanding of how business transactions flow through operational systems and ultimately affect financial reporting or accounting. Experience with Snowflake and large-scale analytical datasets. Experience with data engineering technologies and concepts including ETL/ELT, data pipelines, data modelling, data quality, and orchestration. Experience with Apache Spark / PySpark. Exposure to event-driven systems such as Kafka. Experience with NoSQL databases such as Cassandra or similar distributed data stores. Experience with cloud platforms such as AWS, GCP, or Azure. Experience with containers and orchestration technologies such as Docker and Kubernetes. Experience building CI/CD pipelines and automated deployment processes. Experience with frontend testing frameworks such as Jest, React Testing Library, or Playwright. Experience with Python testing frameworks such as pytest. Understanding of caching, asynchronous processing, concurrency, API performance optimization, and distributed system patterns. Experience implementing observability through metrics, structured logging, tracing, and alerting. Experience reviewing code written by other engineers. Previous experience mentoring interns or junior engineers. Ability to take an ambiguous requirement and independently drive it through technical design and implementation.

Description

You will work within a globally distributed engineering organization supporting Finance business processes and analytical applications. The role involves building applications that bring together data from multiple enterprise sources and convert it into actionable reporting, analytics, insights, and decision-support capabilities. You will work closely with Finance business partners, data engineers, software engineers, product owners, architects, and other Apple engineering teams across India, the United States, Europe, and APAC. You should be comfortable working across the stack — from backend APIs and data processing to databases and modern user interfaces — while maintaining high standards for software quality, performance, reliability, security, and maintainability.

Responsibilities

Design, develop, test, and operate scalable reporting and analytical applications for Apple Finance business users. Build backend services and APIs primarily using Python and FastAPI. Develop responsive, intuitive web applications using React, JavaScript/TypeScript, and Node.js. Design APIs and services that expose analytical, transactional, and financial data securely and efficiently. Build data processing and integration components connecting enterprise databases, data warehouses, APIs, and event-driven systems. Work with large datasets using SQL and modern analytical data platforms. Collaborate directly with Finance partners to understand business processes, reporting requirements, pain points, and opportunities for automation. Translate business requirements into well-designed technical solutions. Participate in architecture and detailed design discussions. Own assigned applications or components from design through development, testing, deployment, production support, and continuous improvement. Write clean, modular, reusable, and well-tested production-quality code. Perform thoughtful code reviews and help improve engineering standards across the team. Diagnose production issues and identify root causes rather than addressing symptoms only. Design systems with reliability, scalability, observability, performance, security, and maintainability in mind. Develop automated unit, integration, API, and UI tests. Contribute to CI/CD pipelines and modern deployment practices. Partner with data engineering teams on ingestion, transformation, data quality, and analytical datasets where required. Mentor interns and junior engineers through design discussions, code reviews, debugging, and engineering best practices.