Data Backend Test Engineer, Apple Media Products

Seattle, Washington, United States
Software and Services

Summary

Posted:
Weekly Hours: 40
Role Number:200391020
The Apple Media Products (AMP) Engineering team is one of the most exciting examples of Apple’s long-held passion for combining art and technology. These are the people who power the App Store, Apple TV, Apple Music, Apple Podcasts, and Apple Books. And they do it on a massive scale, meeting Apple’s high expectations with high performance to deliver a huge variety of entertainment in over 35 languages to more than 150 countries. These engineers build secure, end-to-end solutions. They develop the custom software used to process all the creative work, the tools that providers use to deliver that media, all the server-side systems, and the APIs for many Apple services. Thanks to Apple’s unique integration of hardware, software, and services, engineers here partner to get behind a single unified vision. That vision always includes a deep commitment to strengthening Apple’s privacy policy, one of Apple’s core values. Although services are a bigger part of Apple’s business than ever before, these teams remain small, nimble, and cross-functional, offering greater exposure to the array of opportunities here.

Key Qualifications

  • Day-to-day work involves understanding near-real-time (NRT) and batch data pipeline systems developed by engineering teams for AMP products.
  • Carry out data profiling and understand schema, data interrelationships, and data flows using SparkSQL.
  • Documenting test plans, writing test case automation and working closely with other teams (engineering, project management, etc.)
  • The candidate should possess the ability to implement automated tests for data pipelines using QA automation tools, Java, Spark, JUnit, and IntelliJ
  • Strong proficiency in performing Smoke, Performance, End-to-End, Positive/Negative, Regression, and Functional testing.
  • Interest in AMP products, improving AMP data and learning AMP tools and technologies
  • Leading junior team members.

Description

The Apple Media Products (AMP) Analytics Engineering QA team is responsible for ensuring the Quality and integrity of the data collected and reported on customer experience data. We are seeking Mid-level Data Test Automation Engineers who are interested in AMP products, want to make a difference to them and to Apple as a whole, improve the data quality, and learn AMP's cutting-edge tools and technologies. An ideal candidate brings curiosity, a passion for data, a deep understanding of the technologies behind data pipelines, warehousing, big data, analytics, and excellent knowledge of the Software Development Life Cycle. The Test Engineer will be focused on back-end automation testing and will be responsible for preparing, developing, and executing both manual and automation test strategies with a particular focus on the company’s automation goals. This position is a heavy focus on API testing and data validation.

Education & Experience

BS/BA/MS/MA/Ph.D. degree in any STEM majors (Sciences/Physics/Chem/CS/Math/Statistics/Engineering). Ideally, 5+ years of experience.

Additional Requirements

  • 3+ yrs of Big Data technologies (e.g. Kafka, HDFS, Spark)
  • 3+ yrs experience in Java. Excellent software engineering skills needed. Good knowledge of data structures, streaming, and coding skills are a must.
  • 3+ yrs of experience working with automation tools/frameworks (e.g., Spring, Selenium, JUnit, RestAssured, Gradle, etc.). If you know Spring for Kafka, you are a great fit!
  • 3+ yrs of experience automating APIs using REST Assured Libraries
  • 3+ years of experience configuring and maintaining CI/CD pipelines to automate the test process (strong knowledge of Jenkins and GIT).
  • 3+ years of practical knowledge working in Cloud Environments.
  • 3+ yrs experience with Big Data query tools (e.g., Cassandra, HDFS, SparkSQL)
  • 3+ yrs experience in Data Quality, Data Profiling, and Data Integration tools.
  • 3+ yrs experience in building/designing/testing Data Pipelines