Storage Analytics - Data Engineer
Apple Inc
Herzliya, Israel
Job posting number: #7134376 (Ref:apl-200467892)
Posted: March 7, 2023
Application Deadline: Open Until Filled
Job Description
Summary
Imagine what you could do here. At Apple, new ideas have a way of becoming great products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Our Applications team is seeking a macOS/iOS software engineer. You should have strong SW engineering skills, creative problems solving, passion and motivation to deliver great apps!
We're looking for an exceptional data analyst with a strong background in distributed data processing and a demonstrated ability to turn data into actionable insights. As a data analyst for our Storage Data Analytics team at Apple, you'll play a crucial role in developing the fastest and most efficient SSDs for our products, including the iPhone, iPad, Apple Watch, and new Apple Silicon Macs. If you're passionate about continuously improving the ways we use data to make Apple's products amazing, we want to hear from you!
We're looking for an exceptional data analyst with a strong background in distributed data processing and a demonstrated ability to turn data into actionable insights. As a data analyst for our Storage Data Analytics team at Apple, you'll play a crucial role in developing the fastest and most efficient SSDs for our products, including the iPhone, iPad, Apple Watch, and new Apple Silicon Macs. If you're passionate about continuously improving the ways we use data to make Apple's products amazing, we want to hear from you!
Key Qualifications
- Ability to lead data investigations and analysis projects with ambiguous requirements
- Advanced statistic and modeling knowlege
- Experience visualizing and presenting analyses in tools like Jupyter and Tableau
- Experience programming in Python in a professional setting
- Familiarity with distributed computation, storage, and workflow management (e.g. Splunk, Spark, Kubernetes, Kafka, Hadoop, MapReduce, AirFlow etc.)
- Experience with relational databases and SQL
- Highly organized, creative, motivated, and passionate about achieving results
- Curious and have excellent analytical and problem solving skills
- Excellent written, verbal, and data communication skills
Description
On the Storage Data Analytics team, we are responsible for performing ad hoc data analysis, development and maintenance of distributed data pipelines, as well as creation and support of analysis tools. Our team focuses on Apple’s storage solutions. It is small and nimble, able to quickly explore ideas and present them to the storage teams and to leadership.
In this role you will be:
Building data pipelines to extract data from HDFS for transformation in Spark, and delivery to Postgres and Tableau
Performing exploratory data analysis to evaluate feasibility of investigations
Using Jupyter and Tableau to explore and visualize data for publication to client teams
Delivering analyses via self-service dashboards and in-person presentations with other teams and engineering leaders
Participating in periodic infrastructure support for our team’s Python-based, distributed micro services
Developing storage domain expertise to become a bridge between the storage and the data worlds
In this role you will be:
Building data pipelines to extract data from HDFS for transformation in Spark, and delivery to Postgres and Tableau
Performing exploratory data analysis to evaluate feasibility of investigations
Using Jupyter and Tableau to explore and visualize data for publication to client teams
Delivering analyses via self-service dashboards and in-person presentations with other teams and engineering leaders
Participating in periodic infrastructure support for our team’s Python-based, distributed micro services
Developing storage domain expertise to become a bridge between the storage and the data worlds
Education & Experience
Bachelor's or Master's degree in computer science, data science, statistics, or a related field.
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