Software Development Engineer - Systems

Apple•Austin, TX

About The Position

Imagine what you can do here. Apple is a place where extraordinary people gather to do their lives best work. Together we create products and experiences people once couldn’t have imagined, and now, can’t imagine living without. It’s the diversity of those people and their ideas that inspires the innovation that runs through everything we do. DESCRIPTION APPLE INC has the following available in Austin, Texas. Design and implement systems and tools to support fraud prevention efforts. Oversee test automation and release management to help SDS Engineering mitigate fraud, waste, and abuse company-wide. Design innovative software solutions to open-ended problems using C++, Python, Java, or Scala. Build large-scale big data pipelines & deploy machine learning algorithms using Scala, Spark & Kafka for farmed account & spam detection, ensuring account trust across the Apple ecosystem. Design test strategy and implement test automation for internal tools and fraud services. Develop processes and tools to enable safe and scalable deployment for fraud services. Introduce new data pipelines into the Subs ecosystem, lead the design, architecture and implementation, as well as collaborate with QA & SRE for testing & production deployment to serve datasets for downstream consumers. Make extensive contributions to re-platforming initiative eliminating performance bottleneck, including novel migration from legacy orchestrator and additions to internal developer libraries & tooling using Scala, Spark & SQL. Develop scalable, containerized real-time reaction solutions using Scala, Play & Kafka, partnering with other dev teams & QA to successfully launch production workloads & onboarding team members to the project. Automate end-to-end Python bulk processing workflows to integrate with existing data validation pipelines & APIs. Modernize CI/CD pipelines to industry standards for dependency management and improved debugging efficiency. Leverage SQL query engines including Trino to write memory-efficient relational database queries. 40 hours/week.

Requirements

Bachelor’s degree or foreign equivalent in Computer Science, Computer and Information Technology, or a related field.
Using Python, Java, Clojure, or Scala to build and implement scalable, reliable solutions for building big data pipelines and automations that enable analytics.
Utilizing Agile software development process, Test Driven Development & Continuous Integration to efficiently drive program priorities and needs
Using CICD pipelines (e.g. Jenkins) and test frameworks (e.g. Pytest) for Software Testing & Test Automation to ensure safety and reliability of software solutions
Building Data Science or Data Analysis Tools to optimize and empower efficiency in the downstream consumption of big data pipelines.
Using AWS/Cloud to help support and build a foundational infrastructure for creating big data pipelines and enabling downstream analytics.
Building specialized software solutions that integrate with third-party API services and facilitate the automation of critical analytic solutions.
Building end-to-end automated solutions that monitor and ensure integrity and reliable operations, and can be used to support critical production systems.

Nice To Haves

Responsibilities

Design and implement systems and tools to support fraud prevention efforts.
Oversee test automation and release management to help SDS Engineering mitigate fraud, waste, and abuse company-wide.
Design innovative software solutions to open-ended problems using C++, Python, Java, or Scala.
Build large-scale big data pipelines & deploy machine learning algorithms using Scala, Spark & Kafka for farmed account & spam detection, ensuring account trust across the Apple ecosystem.
Design test strategy and implement test automation for internal tools and fraud services.
Develop processes and tools to enable safe and scalable deployment for fraud services.
Introduce new data pipelines into the Subs ecosystem, lead the design, architecture and implementation, as well as collaborate with QA & SRE for testing & production deployment to serve datasets for downstream consumers.
Make extensive contributions to re-platforming initiative eliminating performance bottleneck, including novel migration from legacy orchestrator and additions to internal developer libraries & tooling using Scala, Spark & SQL.
Develop scalable, containerized real-time reaction solutions using Scala, Play & Kafka, partnering with other dev teams & QA to successfully launch production workloads & onboarding team members to the project.
Automate end-to-end Python bulk processing workflows to integrate with existing data validation pipelines & APIs.
Modernize CI/CD pipelines to industry standards for dependency management and improved debugging efficiency.
Leverage SQL query engines including Trino to write memory-efficient relational database queries.